Data mining with decision trees: Theory and applications

Data Mining with Decision Trees: Theory and Applications

Volumn , Issue , 2007, Pages 1-244

  Rokach, Lior ^a   Maimon, Oded ^b  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85051715472     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/6604     Document Type: Book

References (488)

1. Aha, D. W.; Kibler, D.; and Albert, M. K., Instancebased learning algorithms. Machine Learning 6(1):37-66, 1991.
2. A1-Sultan K. S., A tabu search approach to the clustering problem, Pattern Recognition, 28:1443-1451, 1995.
3. Al-Sultan K. S., Khan M. M.: Computational experience on four algorithms for the hard clustering problem. Pattern Recognition Letters 17(3): 295-308, 1996.
4. Ali K. M., Pazzani M. J., Error Reduction through Learning Multiple Descriptions, Machine Learning, 24: 3, 173-202, 1996.
5. Almuallim H., An Efficient Algorithm for Optimal Pruning of Decision Trees. Artificial Intelligence 83(2): 347-362, 1996.
6. Almuallim H., and Dietterich T.G., Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69: 1-2, 279-306, 1994.
7. Alsabti K., Ranka S. and Singh V., CLOUDS: A Decision Tree Classifier for Large Datasets, Conference on Knowledge Discovery and Data Mining (KDD-98), August 1998.
8. An A. and Wang Y., “Comparisons of classification methods for screening potential compounds”. In IEEE International Conference on Data Mining, 2001.
9. Anand R, Methrotra K, Mohan CK, Ranka S. Efficient classification for multiclass problems using modular neural networks. IEEE Trans Neural Networks, 6(1): 117-125, 1995.
10. Anderson, J.A. and Rosenfeld, E. Talking Nets: An Oral History of Neural Network Research. Cambridge, MA: MIT Press, 2000.
11. Ashenhurst, R. L., The decomposition of switching functions, Technical report, Bell Laboratories BL-1(11), pp. 541-602, 1952.
12. Athanasopoulos, D. (1991). Probabilistic Theory. Stamoulis, Piraeus.
13. Attneave F., Applications of Information Theory to Psychology. Holt, Rinehart and Winston, 1959.
14. Averbuch M., Maimon O., Rokach L., and Ezer E., Free-Text Information Retrieval System for a Rapid Enrollment of Patients into Clinical Trials, Clinical Pharmacology and Therapeutics, 77(2): 13-14, 2005.
15. Avnimelech R. and Intrator N., Boosted Mixture of Experts: an ensemble learning scheme, Neural Computations, 11(2):483-497, 1999.
16. Baker E., and Jain A. K., On feature ordering in practice and some finite sample effects. In Proceedings of the Third International Joint Conference on Pattern Recognition, pages 45-49, San Diego, CA, 1976.
17. Bala J., Huang J., Vafaie H., De Jong K., Wechsler H., Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification, IJCAI conference, 1995.
18. R. Banfield, OpenDT, http://opendt.sourceforge.net/, 2005.
19. Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W.P. Kegelmeyer, A Comparison of Decision Tree Ensemble Creation Techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 173-180, Jan., 2007
20. Banfield J. D. and Raftery A. E., Model-based Gaussian and non-Gaussian clustering. Biometrics, 49:803-821, 1993.
21. Bartlett P. and Shawe-Taylor J., Generalization Performance of Support Vector Machines and Other Pattern Classifiers, In “Advances in Kernel Methods, Support Vector Learning”, Bernhard Scholkopf, Christopher J. C. Burges, and Alexander J. Smola (eds.), MIT Press, Cambridge, USA, 1998.
22. Basak J., Online adaptive decision trees, Neural Computations, 16(9):1959-1981, 2004.
23. Basak J., Online Adaptive Decision Trees: Pattern Classification and Function Approximation, Neural Computations, 18(9):2062-2101, 2006.
24. Bauer, E. and Kohavi, R., “An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants”. Machine Learning, 35: 138, 1999.
25. Baxt, W. G., Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion. Neural Computation, 2(4):480-489, 1990.
26. Bay, S., Nearest neighbor classification from multiple feature subsets. Intelligent Data Analysis, 3(3): 191-209, 1999.
27. Bellman, R., Adaptive Control Processes: A Guided Tour, Princeton University Press, 1961.
28. BenBassat M., Myopic policies in sequential classification. IEEE Trans. on Computing, 27(2):170-174, February 1978.
29. Kristin P. Bennett and Ayhan Demiriz and Richard Maclin, Exploiting unlabeled data in ensemble methods, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 289-296, ACM Press, New York, NY, USA, 2002.
30. Bennett X. and Mangasarian O.L., Multicategory discrimination via linear programming. Optimization Methods and Software, 3:29-39, 1994.
31. Bensusan H. and Kalousis A., Estimating the Predictive Accuracy of a Classifier, In Proc. Proceedings of the 12th European Conference on Machine Learning, pages 25-36, 2001.
32. Bentley J. L. and Friedman J. H., Fast algorithms for constructing minimal spanning trees in coordinate spaces. IEEE Transactions on Computers, C-27(2):97-105, February 1978. 275
33. Bernard M.E., Decision trees and diagrams. Computing Surveys, 14(4):593-623, 1982.
34. Berry M., and Linoff G., Mastering Data Mining, John Wiley & Sons, 2000.
35. Bhargava H. K., Data Mining by Decomposition: Adaptive Search for Hypothesis Generation, INFORMS Journal on Computing Vol. 11, Iss. 3, pp. 239-47, 1999.
36. Biermann, A. W., Faireld, J., and Beres, T. (1982). Signature table systems and learning. IEEE Trans. Syst. Man Cybern., 12(5):635-648.
37. Black, M. and Hickey, R.J., Maintaining the Performance of a Learned Classifier under Concept Drift, Intelligent Data Analysis 3(1), pp. 453-474, 1999.
38. Blum, A. L. and Langley, P., 1997, Selection of relevant features and examples in machine learning, Artificial Intelligence, 97, pp.245-271.
39. Blum A., and Mitchell T., Combining Labeled and Unlabeled Data with CoTraining. In Proc. of the 11th Annual Conference on Computational Learning Theory, pages 92-100, 1998.
40. Bonner, R., On Some Clustering Techniques. IBM journal of research and development, 8:22-32, 1964.
41. Booker L., Goldberg D. E., and Holland J. H., Classifier systems and genetic algorithms. Artificial Intelligence, 40(1-3):235-282, 1989.
42. Brachman, R. and Anand, T., 1994, The process of knowledge discovery in databases, in: Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, pp. 37-58.
43. Bratko I., and Bohanec M., Trading accuracy for simplicity in decision trees, Machine Learning 15: 223-250, 1994.
44. Brazdil P., Gama J., Henery R., Characterizing the Applicability of Classification Algorithms using Meta Level Learning, in Machine Learning: ECML-94, F. Bergadano e L. de Raedt (eds.), LNAI No. 784: pp. 83-102, Springer Verlag, 1994.
45. Breiman L., Bagging predictors, Machine Learning, 24(2):123-140, 1996.
46. Breiman, L., Random forests. Machine Learning, 45, 532, 2001.
47. Breiman L., Friedman J., Olshen R., and Stone C., Classification and Regression Trees. Wadsworth Int. Group, 1984.
48. Br Brodley, C. E., Automatic selection of split criterion during tree growing based on node selection. In Proceedings of the Twelth International Conference on Machine Learning, 73-80 Taho City, Ca. Morgan Kaufmann, 1995.
49. Brodley C. E. and Utgoff P. E., Multivariate decision trees. Machine Learning, 19:45-77, 1995.
50. Brown G., Wyatt J. L., Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. Multiple Classifier Systems 2003: 266-275
51. Brown G., Wyatt J., Harris R., Yao X., Diversity creation methods: a survey and categorisation, Information Fusion, 6(1):5-20.
52. Bruzzone L., Cossu R., Vernazza G., Detection of land-cover transitions by combining multidate classifiers, Pattern Recognition Letters, 25(13): 1491-1500, 2004.
53. Buchanan, B.G. and Shortliffe, E.H., Rule Based Expert Systems, 272-292, Addison-Wesley, 1984.
54. 2 loss: Regression and classification, Journal of the American Statistical Association, 98, 324-338. 2003.
55. Buja, A. and Lee, Y.S., Data mining criteria for tree based regression and classification, Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, (pp 27-36), San Diego, USA, 2001.
56. Buntine, W., A Theory of Learning Classification Rules. Doctoral dissertation. School of Computing Science, University of Technology. Sydney. Australia, 1990.
57. Buntine, W. (1992), “Learning Classification Trees”, Statistics and Computing, 2, 63-73.
58. Buntine, W., “Graphical Models for Discovering Knowledge”, in U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pp 59-82. AAAI/MIT Press, 1996.
59. Buntine W., Niblett T., A Further Comparison of Splitting Rules for Decision-Tree Induction. Machine Learning, 8: 75-85, 1992.
60. Buczak A. L. and Ziarko W., “Neural and Rough Set Based Data Mining Methods in Engineering”, Klosgen W. and Zytkow J. M. (Eds.), Handbook of Data Mining and Knowledge Discovery, pages 788-797. Oxford University Press, 2002.
61. Califf M.E. and Mooney R.J., Relational learning of pattern-match rules for information extraction. Proceedings of the Sixteenth National Conf. on Artificial Intelligence, page 328-334, 1999.
62. Can F., Incremental clustering for dynamic information processing, in ACM Transactions on Information Systems, no. 11, pp 143-164, 1993.
63. Cantu-Paz E., Kamath C., Inducing oblique decision trees with evolutionary algorithms, IEEE Trans. on Evol. Computation 7(1), pp. 54-68, 2003.
64. Cardie, C. (1995). Using decision trees to improve cased- based learning. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining. AAAI Press.
65. Caropreso M., Matwin S., and Sebastiani F., A learner-independent evaluation of the useful-ness of statistical phrases for automated text categorization, Text Databases and Document Management: Theory and Practice. Idea Group Publishing, page 78-102, 2001.
66. Caruana R., Niculescu-Mizil A., Crew G., Ksikes A., Ensemble selection from libraries of models, Twenty-first international conference on Machine learning, July 04-08, 2004, Banff, Alberta, Canada.
67. Carvalho D.R., Freitas A.A., A hybrid decision-tree - genetic algorithm method for data mining, Information Science 163, 13-35, 2004.
68. Catlett J., Mega induction: Machine Learning on Vary Large Databases, PhD, University of Sydney, 1991.
69. Chai B., Huang T., Zhuang X., Zhao Y., Sklansky J., Piecewise-linear classifiers using binary tree structure and genetic algorithm, Pattern Recognition 29(11), pp. 1905-1917, 1996.
70. Chan P. K. and Stolfo, S. J., Toward parallel and distributed learning by metalearning, In AAAI Workshop in Knowledge Discovery in Databases, pp. 227-240, 1993.
71. Chan P.K. and Stolfo, S.J., A Comparative Evaluation of Voting and Metalearning on Partitioned Data, Proc. 12th Intl. Conf. On Machine Learning ICML 95, 1995.
72. Chan P.K. and Stolfo S.J, On the Accuracy of Meta-learning for Scalable Data Mining, J. Intelligent Information Systems, 8:5-28, 1997.
73. Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, 2(6):429-444, 1978.
74. Chawla N. V., Moore T. E., Hall L. O., Bowyer K. W., Springer C., and Kegelmeyer W. P.. Distributed learning with bagging-like performance. Pattern Recognition Letters, 24(1-3):455-471, 2002.
75. Chawla N. V., Hall L. O., Bowyer K. W., Kegelmeyer W. P., Learning Ensembles from Bites: A Scalable and Accurate Approach, The Journal of Machine Learning Research archive, 5:421-451, 2004.
76. Chen K., Wang L. and Chi H., Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification, International Journal of Pattern Recognition and Artificial Intelligence, 11(3): 417-445, 1997.
77. Cheeseman P., Stutz J.: Bayesian Classification (AutoClass): Theory and Results. Advances in Knowledge Discovery and Data Mining 1996: 153-180
78. Cherkauer, K.J., Human Expert-Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks. In Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms Workshop, Thirteenth National Conference on Artificial Intelligence. Portland, OR: AAAI Press, 1996.
79. Cherkauer, K. J. and Shavlik, J. W., Growing simpler decision trees to facilitate knowledge discovery. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. AAAI Press, 1996.
80. Chizi, B., Maimon, O. and Smilovici A. On Dimensionality Reduction of High Dimensional Data Sets, Frontiers in Artificial Intelligence and Applications, IOS press, pp. 230-236, 2002.
81. Christensen S. W., Sinclair I., Reed P. A. S., Designing committees of models through deliberate weighting of data points, The Journal of Machine Learning Research, 4(1):39-66, 2004.
82. Cios K. J. and Sztandera L. M., Continuous ID3 algorithm with fuzzy entropy measures, Proc. IEEE Internat. Con/i on Fuzz’ Systems, 1992, pp. 469-476.
83. Clark P., and Niblett T., The CN2 rule induction algorithm. Machine Learning, 3:261-284, 1989.
84. Clark, P. and Boswell, R., “Rule induction with CN2: Some recent improvements.” In Proceedings of the European Working Session on Learning, pp. 151-163, Pitman, 1991.
85. Clearwater, S., T. Cheng, H. Hirsh, and B. Buchanan. Incremental batch learning. In Proceedings of the Sixth International Workshop on Machine Learning, San Mateo CA:, pp. 366-370. Morgan Kaufmann, 1989.
86. Clemen R., Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5:559-583, 1989
87. S. Cohen, L. Rokach, O. Maimon, Decision Tree Instance Space Decomposition with Grouped Gain-Ratio, Information Science, Volume 177, Issue 17, pp. 3592-3612, 2007.
88. Coppock D. S., Data Modeling and Mining: Why Lift?, Published in DM Review online, June 2002.
89. Crawford S. L., Extensions to the CART algorithm. Int. J. of Man Machine Studies, 31(2):197-217, August 1989.
90. Cunningham P., and Carney J., Diversity Versus Quality in Classification Ensembles Based on Feature Selection, In: R. L. De Mntaras and E. Plaza (eds.), Proc. Ecml 2000, 11th European Conf. On Machine Learning, Barcelona, Spain, LNCS 1810, Springer, 2000, pp. 109-116.
91. Curtis, H. A., A New Approach to the Design of Switching Functions, Van Nostrand, Princeton, 1962.
92. Dhillon I. And Modha D., Concept Decomposition for Large Sparse Text Data Using Clustering. Machine Learning. 42, pp.143-175. (2001).
93. Dempster A.P., Laird N.M., and Rubin D.B., Maximum likelihood from incomplete data using the EM algorithm. Journal of the Royal Statistical Society, 39(B), 1977.
94. Derbeko P., El-Yaniv R. And Meir R., Variance optimized bagging, European Conference on Machine Learning, 2002.
95. Dietterich, T. G., “Approximate statistical tests for comparing supervised classification learning algorithms”. Neural Computation, 10(7): 1895-1924, 1998.
96. Dietterich, T. G., An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting and Randomization. 40(2):139-157, 2000.
97. Dietterich T., Ensemble methods in machine learning. In J. Kittler and F. Roll, editors, First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, pages 1-15. Springer-Verlag, 2000
98. Dietterich, T. G., and Ghulum Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263-286, 1995.
99. Dietterich, T. G., and Kong, E. B., Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Tech. Rep., Oregon State University, 1995.
100. Dietterich, T. G., and Michalski, R. S., A comparative review of selected methods for learning from examples, Machine Learning, an Artificial Intelligence approach, 1: 41-81, 1983.
101. Dietterich, T. G., Kearns, M., and Mansour, Y., Applying the weak learning framework to understand and improve C4.5. Proceedings of the Thirteenth International Conference on Machine Learning, pp. 96-104, San Francisco: Morgan Kaufmann, 1996.
102. Dimitriadou E., Weingessel A., Hornik K., A cluster ensembles framework, Design and application of hybrid intelligent systems, IOS Press, Amsterdam, The Netherlands, 2003.
103. Domingos, P., Using Partitioning to Speed Up Specific-to-General Rule Induction. In Proceedings of the AAAI-96 Workshop on Integrating Multiple Learned Models, pp. 29-34, AAAI Press, 1996.
104. Dominigos P. (1999): MetaCost: A general method for making classifiers cost sensitive. In proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155-164. Acm Press.
105. Domingos, P. And Hulten, G., Mining Time-Changing Data Streams, Proc. Of KDD-2001, ACM Press, 2001.
106. Domingos, P., & Pazzani, M., On the Optimality of the Naive Bayes Classifier under Zero-One Loss, Machine Learning, 29: 2, 103-130, 1997.
107. Dougherty, J., Kohavi, R, Sahami, M., Supervised and unsupervised discretization of continuous attributes. Machine Learning: Proceedings of the twelfth International Conference, Morgan Kaufman pp. 194-202, 1995.
108. Duda, R., and Hart, P., Pattern Classification and Scene Analysis, New-York, Wiley, 1973.
109. Duda, P. E. Hart and D. G. Stork, Pattern Classification, Wiley, New York, 2001.
110. Dunteman, G.H., Principal Components Analysis, Sage Publications, 1989.
111. Dzeroski S., Zenko B., Is Combining Classifiers with Stacking Better than Selecting the Best One?, Machine Learning, 54(3): 255-273, 2004.
112. Elder I. And Pregibon, D., “A Statistical Perspective on Knowledge Discovery in Databases”, In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthu-rusamy editors., Advances in Knowledge Discovery and Data Mining, pp. 83-113, AAAI/MIT Press, 1996.
113. Esmeir, S., and Markovitch, S. 2004. Lookahead-basedalgorithms for anytime induction of decision trees. In ICML04, 257-264.
114. Esposito F., Malerba D. And Semeraro G., A Comparative Analysis of Methods for Pruning Decision Trees. Eee Transactions on Pattern Analysis and Machine Intelligence, 19(5):476-492, 1997.
115. Ester M., Kriegel H.P., Sander S., and Xu X., A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han, and U. Fayyad, editors, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 226-231, Menlo Park, CA, 1996. Aaai, AAAI Press.
116. Estivill-Castro, V. And Yang, J. A Fast and robust general purpose clustering algorithm. Pacific Rim International Conference on Artificial Intelligence, pp. 208-218, 2000.
117. Fraley C. And Raftery A.E., “How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis”, Technical Report No. 329. Department of Statistics University of Washington, 1998.
118. Fayyad, U., Piatesky-Shapiro, G. & Smyth P., From Data Mining to Knowledge Discovery: An Overview. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds), Advances in Knowledge Discovery and Data Mining, pp 1-30, AAAI/MIT Press, 1996.
119. Fayyad, U., Grinstein, G. And Wierse, A., Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001.
120. Fayyad U., and Irani K. B., The attribute selection problem in decision tree generation. In proceedings of Tenth National Conference on Artificial Intelligence, pp. 104-110, Cambridge, MA: AAAI Press/MIT Press, 1992.
121. Feigenbaum E. (1988): Knowledge Processing - From File Servers to Knowledge Servers. In J.R. Queinlan ed., “Applications of Expert Systems”. Vol. 2, Turing Institute Press, Chpater 1, pp. 3-11
122. Ferri C., Flach P., and Hernandez-Orallo J., Learning Decision Trees Using the Area Under the ROC Curve. In Claude Sammut and Achim Hoffmann, editors, Proceedings of the 19th International Conference on Machine Learning, pp. 139-146. Morgan Kaufmann, July 2002
123. Fifield D. J., Distributed Tree Construction From Large Datasets, Bachelor’s Honor Thesis, Australian National University, 1992.
124. Fisher, D., 1987, Knowledge acquisition via incremental conceptual clustering, in machine learning 2, pp. 139-172.
125. Fischer, B., “Decomposition of Time Series - Comparing Different Methods in Theory and Practice”, Eurostat Working Paper, 1995.
126. Fix, E., and Hodges, J.L., Discriminatory analysis. Nonparametric discrimination. Consistency properties. Technical Report 4, US Air Force School of Aviation Medicine. Randolph Field, TX, 1957.
127. Fortier, J.J. And Solomon, H. 1996. Clustering procedures. In proceedings of the Multivariate Analysis, ’ 66, P.R. Krishnaiah (Ed.), pp. 493-506.
128. Fountain, T. Dietterich T., Sudyka B., “Mining IC Test Data to Optimize VLSI Testing”, ACM SIGKDD Conference, 2000, pp. 18-25, 2000.
129. Frank E., Hall M., Holmes G., Kirkby R., Pfahringer B., WEKA - A Machine Learning Workbench for Data Mining, in O. Maimon, L. Rokach, editors The Data Mining and Knowledge Discovery Handbook, Springer, pp. 1305-1314, 2005.
130. Frawley W. J., Piatetsky-Shapiro G., and Matheus C. J., “Knowledge Discovery in Databases: An Overview, ” G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases, 1-27, AAAI Press, Menlo Park, California, 1991.
131. Freitas A. (2005), “Evolutionary Algorithms for Data Mining”, in O. Maimon and L. Rokach (Eds.), The Data Mining and Knowledge Discovery Handbook, Springer, pp. 435-467.
132. Freitas X., and Lavington S. H., Mining Very Large Databases With Parallel Processing, Kluwer Academic Publishers, 1998.
133. Freund Y. And Schapire R. E., Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 325-332, 1996.
134. Friedman J. H., A recursive partitioning decision rule for nonparametric classifiers. Ieee Trans. On Comp., C26:404-408, 1977.
135. Friedman, J. H., “Multivariate Adaptive Regression Splines”, The Annual Of Statistics, 19, 1-141, 1991.
136. Friedman, J.H. (1997A). Data Mining and Statistics: What is the connection? 1997.
137. Friedman, J.H. (1997B). On bias, variance, 0/1 - loss and the curse of dimensionality, Data Mining and Knowledge Discovery, 1: 1, 55-77, 1997.
138. Friedman, J.H. & Tukey, J.W., A Projection Pursuit Algorithm for Exploratory Data Analysis, IEEE Transactions on Computers, 23: 9, 881-889, 1973.
139. Friedman N., Geiger D., and Goldszmidt M., Bayesian Network Classifiers, Machine Learning 29: 2-3, 131-163, 1997.
140. Friedman, J., Kohavi, R., Yun, Y. 1996. Lazy decision trees. Proceedings of the Thirteenth National Conference on Artificial Intelligence. (Pp. 717-724). Cambridge, MA: AAAI Press/MIT Press.
141. Fukunaga, K., Introduction to Statistical Pattern Recognition. San Diego, CA: Academic, 1990.
142. Furnkranz, J., More efficient windowing, In Proceeding of The 14th national Conference on Artificial Intelegence (AAAI-97), pp. 509-514, Providence, RI. Aaai Press, 1997.
143. Gallinari, P., Modular Neural Net Systems, Training of. In (Ed.) M.A. Arbib. The Handbook of Brain Theory and Neural Networks, Bradford Books/MIT Press, 1995.
144. Gama J., A Linear-Bayes Classifier. In C. Monard, editor, Advances on Artificial Intelligence - SBIA2000. Lnai 1952, pp 269-279, Springer Verlag, 2000
145. Gams, M., New Measurements Highlight the Importance of Redundant Knowledge. In European Working Session on Learning, Montpeiller, France, Pitman, 1989.
146. Gago, P. And Bentos, C. (1998). A metric for selection of the most promising rules. In Proceedings of the 2nd European Conference on The Pronciples of Data Mining and Knowledge Discovery (PKDD’98).
147. Gardner M., Bieker, J., Data mining solves tough semiconductor manufacturing problems. Kdd 2000: pp. 376-383, 2000.
148. Gehrke J., Ganti V., Ramakrishnan R., Loh W., BOAT-Optimistic Decision Tree Construction. Sigmod Conference 1999: pp. 169-180, 1999.
149. Gehrke J., Ramakrishnan R., Ganti V., RainForest - A Framework for Fast Decision Tree Construction of Large Datasets, Data Mining and Knowledge Discovery, 4 (2/3) 127-162, 2000.
150. Gelfand S. B., Ravishankar C. S., and Delp E. J., An iterative growing and pruning algorithm for classification tree design. Ieee Transaction on Pattern Analysis and Machine Intelligence, 13(2):163-174, 1991.
151. Geman S., Bienenstock, E., and Doursat, R., Neural networks and the bias/variance dilemma. Neural Computation, 4:1-58, 1995.
152. George, E. And Foster, D. (2000), Calibration and empirical Bayes variable selection, Biometrika, 87(4):731-747.
153. Gilad-Bachrach, R., Navot, A. And Tisliby T. (2004) N. Margin based feature selection - theory and algorithms. Proceeding of the 21’st International Confer-enc on Machine Learning, 2004.
154. Gillo M. W., MAID: A Honeywell 600 program for an automatised survey analysis. Behavioral Science 17: 251-252, 1972.
155. Giraud-Carrier Ch., Vilalta R., Brazdil R., Introduction to the Special Issue of on Meta-Learning, Machine Learning, 54 (3), 197-194, 2004.
156. Gluck, M. And Corter, J. (1985). Information, uncertainty, and the utility of categories. Proceedings of the Seventh Annual Conference of the Cognitive Science Society (pp. 283-287). Irvine, California: Lawrence Erlbaum Associates.
157. Grossman R., Kasif S., Moore R., Rocke D., and Ullman J., Data mining research: Opportunities and challenges. Report of three NSF workshops on mining large, massive, and distributed data, 1999.
158. Grumbach S., Milo T., Towards Tractable Algebras for Bags. Journal of Computer and System Sciences 52(3): 570-588, 1996.
159. Guo Y. And Sutiwaraphun J., Knowledge probing in distributed data mining, in Proc. 4H Int. Conf. Knowledge Discovery Data Mining, pp 61-69, 1998.
160. Guha, S., Rastogi, R. And Shim, K. Cure: An efficient clustering algorithm for large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 73-84, New York, 1998.
161. Gunter S., Bunke H., Feature Selection Algorithms for the generation of multiple classifier systems, Pattern Recognition Letters, 25(11):1323-1336, 2004.
162. Guyon I. And Elisseeff A., “An introduction to variable and feature selection”, Journal of Machine Learning Research 3, pp. 1157-1182, 2003.
163. Hall, M. Correlation- based Feature Selection for Machine Learning. University of Waikato, 1999.
164. Han, J. And Kamber, M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
165. Hancock T. R., Jiang T., Li M., Tromp J., Lower Bounds on Learning Decision Lists and Trees. Information and Computation 126(2): 114-122, 1996.
166. Hand, D., Data Mining - reaching beyond statistics, Research in Official Stat. 1(2):5-17, 1998.
167. Hampshire, J. B., and Waibel, A. The meta-Pi network - building distributed knowledge representations for robust multisource pattern-recognition. Pattern Analyses and Machine Intelligence 14(7): 751-769, 1992.
168. Hansen J., Combining Predictors. Meta Machine Learning Methods and Bias/Variance & Ambiguity Decompositions. Phd dissertation. Aurhus University. 2000.
169. Hansen, L. K., and Salamon, P., Neural network ensembles. Ieee Transactions on Pattern Analysis and Machine Intelligence, 12(10), 993-1001, 1990.
170. Hartigan, J. A. Clustering algorithms. John Wiley and Sons., 1975.
171. Huang, Z., Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 1998.
172. He D. W., Strege B., Tolle H., and Kusiak A., Decomposition in Automatic Generation of Petri Nets for Manufacturing System Control and Scheduling, International Journal of Production Research, 38(6): 1437-1457, 2000.
173. Hilderman, R. And Hamilton, H. (1999). Knowledge discovery and interestingness measures: A survey. In Technical Report CS 99-04.Department of Computer Science, University of Regina.
174. Ho T. K., The Random Subspace Method for Constructing Decision Forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, 1998, pp. 832-844.
175. Holmes, G. And Nevill-Manning, C. G. (1995). Feature selection via the discovery of simple classification rules. In Proceedings of the Symposium on Intelligent Data Analysis, Baden- Baden, Germany.
176. Holmstrom, L., Koistinen, P., Laaksonen, J., and Oja, E., Neural and statistical classifiers - taxonomy and a case study. Ieee Trans. On Neural Networks, 8, :5-17, 1997.
177. Holte R. C., Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-90, 1993.
178. Holte, R. C.; Acker, L. E.; and Porter, B. W., Concept learning and the problem of small disjuncts. In Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 813-818, 1989.
179. Hong S., Use of Contextual Information for Feature Ranking and Discretization, IEEE Transactions on Knowledge and Data Engineering, 9(5):718-730, 1997.
180. Hoppner F., Klawonn F., Kruse R., Runkler T., Fuzzy Cluster Analysis, Wiley, 2000.
181. Hrycej T., Modular Learning in Neural Networks. New York: Wiley, 1992.
182. Hu, X., Using Rough Sets Theory and Database Operations to Construct a Good Ensemble of Classifiers for Data Mining Applications. Icdm01. Pp 233-240, 2001.
183. Hubert, L. And Arabie, P. (1985) Comparing partitions. Journal of Classification, 5. 193-218.
184. Hunter L., Klein T. E., Finding Relevant Biomolecular Features. Ismb 1993, pp. 190-197, 1993.
185. Hwang J., Lay S., and Lippman A., Nonparametric multivariate density estimation: A comparative study, IEEE Transaction on Signal Processing, 42(10): 2795-2810, 1994.
186. Hyafil L. And Rivest R.L., Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5(1):15-17, 1976
187. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. Adaptive mixtures of local experts. Neural Computation 3(1):79-87, 1991.
188. Jackson, J., A User’s Guide to Principal Components.New York: John Wiley and Sons, 1991.
189. Jang J., “Structure determination in fuzzy modeling: A fuzzy CART approach, ” in Proc. Ieee Conf. Fuzzy Systems, 1994, pp. 480-485.
190. Janikow, C.Z., Fuzzy Decision Trees: Issues and Methods, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 28, Issue 1, pp. 1-14. 1998.
191. Jain, A.K. Murty, M.N. And Flynn, P.J. Data Clustering: A Survey. Acm Computing Surveys, Vol. 31, No. 3, September 1999.
192. Jain, A. And Zonker, D., Feature Selection: Evaluation, Application, and Small Sample Performance. Ieee Trans. On Pattern Analysis and Machine Intelligence, 19, 153-158, 1997.
193. Jenkins R. And Yuhas, B. P. A simplified neural network solution through problem decomposition: The case of Truck backer-upper, IEEE Transactions on Neural Networks 4(4):718-722, 1993.
194. Jimenez, L. O., & Landgrebe D. A., Supervised Classification in High- Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariate Data. Ieee Transaction on Systems Man, and Cybernetics — Part C: Applications and Reviews, 28:39-54, 1998.
195. Johansen T. A. And Foss B. A., A narmax model representation for adaptive control based on local model -Modeling, Identification and Control, 13(1):25-39, 1992.
196. John G. H., Robust linear discriminant trees. In D. Fisher and H. Lenz, editors, Learning From Data: Artificial Intelligence and Statistics V, Lecture Notes in Statistics, Chapter 36, pp. 375-385. Springer-Verlag, New York, 1996.
197. John G. H., Kohavi R., and Pfleger P., Irrelevant features and the subset selection problem. In Machine Learning: Proceedings of the Eleventh International Conference. Morgan Kaufmann, 1994.
198. John G. H., and Langley P., Estimating Continuous Distributions in Bayesian Classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. Pp. 338-345. Morgan Kaufmann, San Mateo, 1995.
199. Jordan, M. I., and Jacobs, R. A., Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214, 1994.
200. Jordan, M. I., and Jacobs, R. A. Hierarchies of adaptive experts. In Advances in Neural Information Processing Systems, J. E. Moody, S. J. Hanson, and R. P. Lippmann, Eds., vol. 4, Morgan Kaufmann Publishers, Inc., pp. 985-992, 1992.
201. Joshi, V. M., “On Evaluating Performance of Classifiers for Rare Classes”, Second IEEE International Conference on Data Mining, IEEE Computer Society Press, pp. 641-644, 2002.
202. Kamath, C., and E. Cantu-Paz, Creating ensembles of decision trees through sampling, Proceedings, 33-rd Symposium on the Interface of Computing Science and Statistics, Costa Mesa, CA, June 2001.
203. Kamath, C., Cant-Paz, E. And Littau, D. (2002). Approximate splitting for ensembles of trees using histograms. In Second SIAM International Conference on Data Mining (SDM-2002).
204. Kanal, L. N., “Patterns in Pattern Recognition: 1968-1974”. Ieee Transactions on Information Theory IT-20, 6: 697-722, 1974.
205. Kargupta, H. And Chan P., eds, Advances in Distributed and Parallel Knowledge Discovery, pp. 185-210, AAAI/MIT Press, 2000.
206. Kaufman, L. And Rousseeuw, P.J., 1987, Clustering by Means of Medoids, In Y. Dodge, editor, Statistical Data Analysis, based on the L1 Norm, pp. 405-416, Elsevier/North Holland, Amsterdam.
207. Kaufmann, L. And Rousseeuw, P.J. Finding groups in data. New-York: Wiley, 1990.
208. Kass G. V., An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29(2):119-127, 1980.
209. Kearns M. And Mansour Y., A fast, bottom-up decision tree pruning algorithm with near-optimal generalization, in J. Shavlik, ed., ‘Machine Learning: Proceedings of the Fifteenth International Conference’, Morgan Kaufmann Publishers, Inc., pp. 269-277, 1998.
210. Kearns M. And Mansour Y., On the boosting ability of top-down decision tree learning algorithms. Journal of Computer and Systems Sciences, 58(1): 109-128, 1999.
211. Kenney, J. F. And Keeping, E. S. “Moment-Generating and Characteristic Functions, ” “Some Examples of Moment-Generating Functions, ” and “Uniqueness Theorem for Characteristic Functions.” §4.6-4.8 in Mathematics of Statistics, Pt. 2, 2nd ed. Princeton, NJ: Van Nostrand, pp. 72-77, 1951.
212. Kerber, R., 1992, ChiMerge: Descretization of numeric attributes, in AAAI-92, Proceedings Ninth National Conference on Artificial Intelligence, pp. 123-128, AAAI Press/MIT Press.
213. Kim J.O. & Mueller C.W., Factor Analysis: Statistical Methods and Practical Issues. Sage Publications, 1978.
214. Kim, D.J., Park, Y.W. And Park P. A novel validity index for determination of the optimal number of clusters. Ieice Trans. Inf., Vol. E84-D, no.2 (2001), 281-285.
215. King, B. Step-wise Clustering Procedures, J. Am. Stat. Assoc. 69, pp. 86-101, 1967.
216. Kira, K. And Rendell, L. A., A practical approach to feature selection. In Machine Learning: Proceedings of the Ninth International Conference, 1992.
217. Klosgen W. And Zytkow J. M., “KDD: The Purpose, Necessity and Chalanges”, Klosgen W. And Zytkow J. M. (Eds.), Handbook of Data Mining and Knowledge Discovery, pp. 1-9. Oxford University Press, 2002.
218. Kohavi, R., Bottom-up induction of oblivious read-once decision graphs, in F. Bergadano and L. De Raedt, editors, Proc. European Conference on Machine Learning, pp. 154-169, Springer-Verlag, 1994.
219. Kohavi R., Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 114-119, 1996.
220. Kohavi R., Becker B., and Sommerfield D., Improving simple Bayes. In Proceedings of the European Conference on Machine Learning, 1997.
221. Kohavi R. And John G., The Wrapper Approach, In Feature Extraction, Construction and Selection: A Data Mining Perspective, H. Liu and H. Motoda (eds.), Kluwer Academic Publishers, 1998.
222. Kohavi, R. And Kunz, C. (1997), Option decision trees with majority votes, in D. Fisher, ed., ‘Machine Learning: Proceedings of the Fourteenth International Conference’, Morgan Kaufmann Publishers, Inc., pp. 161-169.
223. Kohavi R., and Provost F., Glossary of Terms, Machine Learning 30(2/3): 271-274, 1998.
224. Kohavi R. And Quinlan J. R., Decision-tree discovery. In Klosgen W. And Zytkow J. M., editors, Handbook of Data Mining and Knowledge Discovery, chapter 16.1.3, pages 267-276. Oxford University Press, 2002.
225. Kohavi R. And Sommerfield D., Targeting business users with decision table classifiers, in R. Agrawal, P. Stolorz & G. Piatetsky-Shapiro, eds, ‘Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining’, AAAI Press, pp. 249-253, 1998.
226. Kohavi R. And Wolpert, D. H., Bias Plus Variance Decomposition for Zero-One Loss Functions, Machine Learning: Proceedings of the 13th International Conference. Morgan Kaufman, 1996.
227. Kolen, J. F., and Pollack, J. B., Back propagation is sesitive to initial conditions. In Advances in Neural Information Processing Systems, Vol. 3, pp. 860-867 San Francisco, CA. Morgan Kaufmann, 1991.
228. Kolcz, A. Chowdhury, and J. Alspector, Data duplication: An imbalance problem In Workshop on Learning from Imbalanced Data Sets” (ICML), 2003.
229. Koller, D. And Sahami, M. (1996). Towards optimal feature selection. In Machine Learning: Proceedings of the Thirteenth International Conference on machine Learning. Morgan Kaufmann, 1996.
230. Kononenko, I., Comparison of inductive and Naive Bayes learning approaches to automatic knowledge acquisition. In B. Wielinga (Ed.), Current Trends in Knowledge Acquisition, Amsterdam, The Netherlands IOS Press, 1990.
231. Kononenko, I., SemiNaive Bayes classifier, Proceedings of the Sixth European Working Session on Learning, pp. 206-219, Porto, Portugal: SpringerVerlag, 1991.
232. Krtowski M., An evolutionary algorithm for oblique decision tree induction, Proc. Of ICAISC’04, Springer, LNCS 3070, pp.432-437, 2004.
233. Krtowski M., Grze M., Global learning of decision trees by an evolutionary algorithm (Khalid Saeed and Jerzy Peja), Information Processing and Security Systems, Springer, pp. 401-410, 2005.
234. Krogh, A., and Vedelsby, J., Neural network ensembles, cross validation and active learning. In Advances in Neural Information Processing Systems 7, pp. 231-238 1995.
235. Kuhn H. W., The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2:83-97, 1955.
236. Kuncheva L.I. Diversity in multiple classifier systems (Editorial), Information Fusion, 6 (1), 2005, 3-4.
237. Kuncheva, L., & Whitaker, C., Measures of diversity in classifier ensembles and their relationship with ensemble accuracy. Machine Learning, pp. 181-207, 2003.
238. Kusiak, A., Decomposition in Data Mining: An Industrial Case Study, IEEE Transactions on Electronics Packaging Manufacturing, Vol. 23, No. 4, pp. 345-353, 2000.
239. Kusiak, A., Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing, IEEE Transactions on Electronics Packaging Manufacturing, 24(1): 44-50, 2001A.
240. Kusiak, A., 2001, Feature Transformation Methods in Data Mining, IEEE Transactions on Elctronics Packaging Manufacturing, Vol. 24, No. 3, pp. 214-221, 2001B.
241. Kusiak A., Kurasek C., Data Mining of Printed-Circuit Board Defects, IEEE Transactions on Robotics and Automation, 17(2): 191-196, 2001.
242. Kusiak, E. Szczerbicki, and K. Park, A Novel Approach to Decomposition of Design Specifications and Search for Solutions, International Journal of Production Research, 29(7): 1391-1406, 1991.
243. Langdon W. B., Barrett S. J., Buxton B. F., Combining decision trees and neural networks for drug discovery, in: Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, Kinsale, Ireland, 2002, pp. 60-70.
244. Langley, P., Selection of relevant features in machine learning, in Proceedings of the AAAI Fall Symposium on Relevance, pp. 140-144, AAAI Press, 1994.
245. Langley, P. And Sage, S., Oblivious decision trees and abstract cases. In Working Notes of the AAAI-94 Workshop on Case-Based Reasoning, pp. 113-117, Seattle, WA: AAAI Press, 1994.
246. Langley, P. And Sage, S., Induction of selective Bayesian classifiers. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 399-406. Seattle, WA: Morgan Kaufmann, 1994.
247. Larsen, B. And Aone, C. 1999. Fast and effective text mining using linear-time document clustering. In Proceedings of the 5th ACM SIGKDD, 16-22, San Diego, CA.
248. Last, M., Online Classification of Nonstationary Data Streams, Intelligent Data Analysis 5, IDA83, pp. 119, 2001.
249. Last M., Kandel A., Data Mining for Process and Quality Control in the Semiconductor Industry, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 207-234, 2001.
250. Last M., Kandel A., Maimon O., Eberbach E., Anytime Algorithm for Feature Selection. Rough Sets and Current Trends in Computing 2000: 532-539
251. Last, M., Maimon, O. And Minkov, E., Improving Stability of Decision Trees, International Journal of Pattern Recognition and Artificial Intelligence, 16(2), 145-159, 2002.
252. Lee, S., Noisy Replication in Skewed Binary Classification, Computational Statistics and Data Analysis, 34, 2000.
253. Leigh W., Purvis R., Ragusa J. M., Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural networks, and genetic algorithm: a case study in romantic decision support, Decision Support Systems 32(4): 361-377, 2002.
254. Lewis D., and Catlett J., Heterogeneous uncertainty sampling for supervised learning. In Machine Learning: Proceedings of the Eleventh Annual Conference, pp. 148-156, New Brunswick, New Jersey, Morgan Kaufmann, 1994.
255. Lewis, D., and Gale, W., Training text classifiers by uncertainty sampling, In seventeenth annual international ACM SIGIR conference on research and development in information retrieval, pp. 3-12, 1994.
256. Li X. And Dubes R. C., Tree classifier design with a Permutation statistic, Pattern Recognition 19:229-235, 1986.
257. Liao Y., and Moody J., Constructing Heterogeneous Committees via Input Feature Grouping, in Advances in Neural Information Processing Systems, Vol.12, S.A. Solla, T.K. Leen and K.-R. Muller (eds.), MIT Press, 2000.
258. Lim X., Loh W.Y., and Shih X., A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning 40:203-228, 2000.
259. Lin Y. K. And FuK., Automatic classification of cervical cells using a binary tree classifier. Pattern Recognition, 16(1):69-80, 1983.
260. Lindbergh D.A.B. And Humphreys B.L., The Unified Medical Language System. In: van Bemmel JH and McCray AT, 1993 Yearbook of Medical Informatics. Imia, the Nether-lands, page 41-51, 1993.
261. Ling C. X., Sheng V. S., Yang Q., Test Strategies for Cost-Sensitive Decision Trees IEEE Transactions on Knowledge and Data Engineering, 18(8):1055-1067, 2006.
262. Liu Y.: Generate Different Neural Networks by Negative Correlation Learning. Icnc (1) 2005: 149-156
263. Liu, H., Hsu, W., and Chen, S. (1997). Using general impressions to analyze discovered classification rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD’97). Newport Beach, California.
264. Liu H., Mandvikar A., Mody J., An Empirical Study of Building Compact Ensembles. Waim 2004: pp. 622-627.
265. Liu H. & Motoda H., Feature Selection for Knowledge Discovery and Data Mining, Kluwer Academic Publishers, 1998.
266. Liu, H. And Setiono, R. (1996) A probabilistic approach to feature selection: A filter solution. In Machine Learning: Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann.
267. Loh W.Y., and Shih X., Split selection methods for classification trees. Statistica Sinica, 7: 815-840, 1997.
268. Loh W.Y. And Shih X., Families of splitting criteria for classification trees. Statistics and Computing 9:309-315, 1999.
269. Loh W.Y. And Vanichsetakul N., Tree-structured classification via generalized discriminant Analysis. Journal of the American Statistical Association, 83:715-728, 1988.
270. Long C., Bi-Decomposition of Function Sets Using Multi-Valued Logic, Eng.Doc. Dissertation, Technischen Universitat Bergakademie Freiberg 2003.
271. Lopez de Mantras R., A distance-based attribute selection measure for decision tree induction, Machine Learning 6:81-92, 1991.
272. Lu B.L., Ito M., Task Decomposition and Module Combination Based on Class Relations: A Modular Neural Network for Pattern Classification, IEEE Trans. On Neural Networks, 10(5):1244-1256, 1999.
273. Lu H., Setiono R., and Liu H., Effective Data Mining Using Neural Networks. Ieee Transactions on Knowledge and Data Engineering, 8 (6): 957-961, 1996.
274. Luba, T., Decomposition of multiple-valued functions, in Intl. Symposium on Multiple-Valued Logic’, Bloomigton, Indiana, pp. 256-261, 1995.
275. Lubinsky D., Algorithmic speedups in growing classification trees by using an additive split criterion. Proc. Ai & Statistics 93, pp. 435-444, 1993.
276. Maher P. E. And Clair D. C, Uncertain reasoning in an ID3 machine learning framework, in Proc. 2Nd IEEE Int. Conf. Fuzzy Systems, 1993, pp. 712.
277. Maimon O. And Last M., Knowledge Discovery and Data Mining: The Info-Fuzzy network (IFN) methodology, Kluwer Academic Publishers, 2000.
278. Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 311-336, 2001.
279. Maimon O. And Rokach L., “Improving supervised learning by feature decomposition”, Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems, Lecture Notes in Computer Science, Springer, pp. 178-196, 2002.
280. Maimon O., Rokach L., Ensemble of Decision Trees for Mining Manufacturing Data Sets, Machine Engineering, vol. 4 No 1-2, 2004.
281. Maimon O., Rokach L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications, World Scientific Publishing, 2005.
282. Mallows, C. L., Some comments on Cp. Technometrics15, 661-676, 1973.
283. Mangiameli P., West D., Rampal R., Model selection for medical diagnosis decision support systems, Decision Support Systems, 36(3): 247-259, 2004.
284. Mansour, Y. And McAllester, D., Generalization Bounds for Decision Trees, in Proceedings of the 13th Annual Conference on Computer Learning Theory, pp. 69-80, San Francisco, Morgan Kaufmann, 2000.
285. Marcotorchino, J.F. And Michaud, P. Optimisation en Analyse Ordinale des Donns. Masson, Paris.
286. Margineantu D. And Dietterich T., Pruning adaptive boosting. In Proc. Fourteenth Intl. Conf. Machine Learning, pages 211-218, 1997.
287. Margineantu, D. (2001). Methods for Cost-Sensitive Learning. Doctoral Dissertation, Oregon State University.
288. Martin J. K., An exact probability metric for decision tree splitting and stopping. An Exact Probability Metric for Decision Tree Splitting and Stopping, Machine Learning, 28 (2-3):257-291, 1997.
289. Mehta M., Rissanen J., Agrawal R., MDL-Based Decision Tree Pruning. Kdd 1995: pp. 216-221, 1995.
290. Mehta M., Agrawal R. And Rissanen J., SLIQ: A fast scalable classifier for data mining: In Proc. If the fifth Int’l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996.
291. Melville P., Mooney R. J., Constructing Diverse Classifier Ensembles using Artificial Training Examples. Ijcai 2003: 505-512
292. Meretakis, D. And Wthrich, B., Extending Nave Bayes Classifiers Using Long Itemsets, in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 165-174, San Diego, USA, 1999.
293. Merkwirth C., Mauser H., Schulz-Gasch T., Roche O., Stahl M., Lengauer T., Ensemble methods for classification in cheminformatics, Journal of Chemical Information and Modeling, 44(6):1971-1978, 2004.
294. Merz, C. J., Using Correspondence Analysis to Combine Classifier, Machine Learning, 36(1-2):33-58, 1999.
295. Merz, C. J. And Murphy P.M., UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science, 1998.
296. Michalski R. S., A theory and methodology of inductive learning. Artificial Intelligence, 20:111-161, 1983.
297. Michalski R. S., Understanding the nature of learning: issues and research directions, in R. Michalski, J. Carbonnel and T. Mitchell, eds, Machine Learning: An Artificial Intelligence Approach, Kaufmann, Paolo Alto, CA, pp. 3-25, 1986.
298. Michalski R. S., and Tecuci G.. Machine Learning, A Multistrategy Approach, Vol. J. Morgan Kaufmann, 1994.
299. Michie, D., Problem decomposition and the learning of skills, in Proceedings of the European Conference on Machine Learning, pp. 17-31, Springer-Verlag, 1995.
300. Michie D., Spiegelhalter D.J., Taylor C.C., Machine Learning, Neural and Statistical Classification, Prentice Hall, 1994.
301. Mingers J., An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4(2):227-243, 1989.
302. Minsky M., Logical vs. Analogical or Symbolic vs. Connectionist or Neat vs. Scruffy, in Artificial Intelligence at MIT., Expanding Frontiers, Patrick H. Winston (Ed.), Vol 1, MIT Press, 1990. Reprinted in AI Magazine, 1991.
303. Mishra, S. K. And Raghavan, V. V., An empirical study of the performance of heuristic methods for clustering. In Pattern Recognition in Practice, E. S. Gelsema and L. N. Kanal, Eds. 425-436, 1994.
304. Mitchell, T., Machine Learning, McGraw-Hill, 1997.
305. Mitchell, T., The need for biases in learning generalizations. Technical Report CBM-TR-117, Rutgers University, Department of Computer Science, New Brunswick, NJ, 1980.
306. Moody, J. And Darken, C., Fast learning in networks of locally tuned units. Neural Computations, 1(2):281-294, 1989.
307. Morgan J. N. And Messenger R. C., THAID: a sequential search program for the analysis of nominal scale dependent variables. Technical report, Institute for Social Research, Univ. Of Michigan, Ann Arbor, MI, 1973.
308. Muller W., and Wysotzki F., Automatic construction of decision trees for classification. Annals of Operations Research, 52:231-247, 1994.
309. Murphy, O. J., and McCraw, R. L. 1991. Designing storage efficient decision trees. Ieee-TC 40(3):315-320.
310. Murtagh, F. A survey of recent advances in hierarchical clustering algorithms which use cluster centers. Comput. J. 26 354-359, 1984.
311. Murthy S. K., Automatic Construction of Decision Trees from Data: A MultiDisciplinary Survey. Data Mining and Knowledge Discovery, 2(4):345-389, 1998.
312. Murthy S. K., Kasif S., and Salzberg S.. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2:1-33, August 1994.
313. Murthy, S. And Salzberg, S. (1995), Lookahead and pathology in decision tree induction, in C. S. Mellish, ed., ‘Proceedings of the 14th International Joint Con- ference on Articial Intelligence’, Morgan Kaufmann, pp. 1025-1031.
314. Myers E.W., An O(ND) Difference Algorithm and Its Variations, Algorithmica, 1(1): page 251-266, 1986.
315. Naumov G.E., NP-completeness of problems of construction of optimal decision trees. Soviet Physics: Doklady, 36(4):270-271, 1991.
316. Neal R., Probabilistic inference using Markov Chain Monte Carlo methods. Tech. Rep. Crg-TR-93-1, Department of Computer Science, University of Toronto, Toronto, CA, 1993.
317. Ng, R. And Han, J. 1994. Very large data bases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB94, Santiago, Chile, Sept.), VLDB Endowment, Berkeley, CA, 144-155.
318. Niblett T., Constructing decision trees in noisy domains. In Proceedings of the Second European Working Session on Learning, pages 67-78, 1987.
319. Niblett T. And Bratko I., Learning Decision Rules in Noisy Domains, Proc. Expert Systems 86, Cambridge: Cambridge University Press, 1986.
320. Nowlan S. J., and Hinton G. E. Evaluation of adaptive mixtures of competing experts. In Advances in Neural Information Processing Systems, R. P. Lipp-mann, J. E. Moody, and D. S. Touretzky, Eds., vol. 3, pp. 774-780, Morgan Kaufmann Publishers Inc., 1991.
321. Nunez, M. (1988): Economic induction: A case study. In D. Sleeman (Ed.), Proceeding of the Third European Working Session on Learning. London: Pitman Publishing
322. Nunez, M. (1991): The use of Background Knowledge in Decision Tree Induction. Machine Learning, 6(1), pp. 231-250.
323. Oates, T., Jensen D., 1998, Large Datasets Lead to Overly Complex Models: An Explanation and a Solution, KDD 1998, pp. 294-298.
324. Ohno-Machado, L., and Musen, M. A. Modular neural networks for medical prognosis: Quantifying the benefits of combining neural networks for survival prediction. Connection Science 9, 1 (1997), 71-86.
325. Olaru C., Wehenkel L., A complete fuzzy decision tree technique, Fuzzy Sets and Systems, 138(2):221-254, 2003.
326. Oliveira L.S., Sabourin R., Bortolozzi F., and Suen C. Y. (2003) A Methodology for Feature Selection using Multi-Objective Genetic Algorithms for Handwritten Digit String Recognition, International Journal of Pattern Recognition and Artificial Intelligence, 17(6):903-930.
327. Opitz, D., Feature Selection for Ensembles, In: Proc. 16Th National Conf. On Artificial Intelligence, AAAI, 1999, pp. 379-384.
328. Opitz, D. And Maclin, R., Popular Ensemble Methods: An Empirical Study, Journal of Artificial Research, 11: 169-198, 1999.
329. Opitz D. And Shavlik J., Generating accurate and diverse members of a neuralnetwork ensemble. In David S. Touretzky, Michael C. Mozer, and Michael E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 535-541. The MIT Press, 1996.
330. Pagallo, G. And Huassler, D., Boolean feature discovery in empirical learning, Machine Learning, 5(1): 71-99, 1990.
331. S. Pang, D. Kim, S. Y. Bang, Membership authentication in the dynamic group by face classification using SVM ensemble. Pattern Recognition Letters, 24: 215-225, 2003.
332. Park C., Cho S., Evolutionary Computation for Optimal Ensemble Classifier in Lymphoma Cancer Classification. 521-530.
333. Ning Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki (Eds.): Foundations of Intelligent Systems, 14th International Symposium, ISMIS 2003, Maebashi City, Japan, October 28-31, 2003, Proceedings. Lecture Notes in Computer Science, pp. 521-530, 2003.
334. Parmanto, B., Munro, P. W., and Doyle, H. R., Improving committee diagnosis with resampling techinques. In Touretzky, D. S., Mozer, M. C., and Hes-selmo, M. E. (Eds). Advances in Neural Information Processing Systems, Vol. 8, pp. 882-888 Cambridge, MA. Mit Press, 1996.
335. th International conference on Machine Learning, 217-25. Morgan Kaufmann.
336. Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann, 1988.
337. Peng, F. And Jacobs R. A., and Tanner M. A., Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Application to Speech Recognition, Journal of the American Statistical Association 91, 953-960, 1996.
338. Peng Y., Intelligent condition monitoring using fuzzy inductive learning, Journal of Intelligent Manufacturing, 15 (3): 373-380, June 2004.
339. Perkowski, M. A., A survey of literature on function decomposition, Technical report, GSRP Wright Laboratories, Ohio OH, 1995.
340. Perkowski, M.A., Luba, T., Grygiel, S., Kolsteren, M., Lisanke, R., Iliev, N., Burkey, P., Burns, M., Malvi, R., Stanley, C., Wang, Z., Wu, H., Yang, F., Zhou, S. And Zhang, J. S., Unified approach to functional decompositions of switching functions, Technical report, Warsaw University of Technology and Eindhoven University of Technology, 1995.
341. Perkowski, M., Jozwiak, L. And Mohamed, S., New approach to learning noisy Boolean functions, Proceedings of the Second International Conference on Computational Intelligence and Multimedia Applications, pp. 693-706, World Scientific, Australia, 1998.
342. Perner P., Improving the Accuracy of Decision Tree Induction by Feature PreSelection, Applied Artificial Intelligence 2001, vol. 15, No. 8, p. 747-760.
343. Pfahringer, B., Controlling constructive induction in CiPF, In Bergadano, F. And De Raedt, L. (Eds.), Proceedings of the seventh European Conference on Machine Learning, pp. 242-256, Springer-Verlag, 1994.
344. Pfahringer, B., Compression- based feature subset selection. In Proceeding of the IJCAI- 95 Workshop on Data Engineering for Inductive Learning, pp. 109-119, 1995.
345. Pfahringer, B., Bensusan H., and Giraud-Carrier C., Tell Me Who Can Learn You and I Can Tell You Who You are: Landmarking Various Learning Algorithms, In Proc. Of the Seventeenth International Conference on Machine Learning (ICML2000), pages 743-750, 2000.
346. Piatetsky-Shapiro, G. (1991). Discovery analysis and presentation of strong rules. Knowledge Discovery in Databases, AAAI/MIT Press.
347. Poggio T., Girosi, F., Networks for Approximation and Learning, Proc. Ieer, Vol 78(9): 1481-1496, Sept. 1990.
348. Pratt, L. Y., Mostow, J., and Kamm C. A., Direct Transfer of Learned Information Among Neural Networks, in: Proceedings of the Ninth National Conference on Artificial Intelligence, Anaheim, CA, 584-589, 1991.
349. Prodromidis, A. L., Stolfo, S. J. And Chan, P. K., Effective and efficient pruning of metaclassifiers in a distributed data mining system. Technical report CUCS-017-99, Columbia Univ., 1999.
350. Provan, G. M. And Singh, M. (1996). Learning Bayesian networks using feature selection. In D. Fisher and H. Lenz, (Eds.), Learning from Data, Lecture Notes in Statistics, pages 291-300. Springer- Verlag, New York.
351. Provost, F. (1994): Goal-Directed Inductive learning: Trading off accuracy for reduced error cost. Aaai Spring Symposium on Goal-Driven Learning.
352. Provost F. And Fawcett T. (1997): Analysis and visualization of Classifier Performance Comparison under Imprecise Class and Cost Distribution. In Proceedings of KDD-97, pages 43-48. Aaai Press.
353. th Intl. Conf. On Machine Learning, pp. 445-453, Madison, WI.
354. Provost, F. And Fawcett, T. (2001), Robust {Classification for {I}mprecise {Environments, Machine Learning, 42/3:203-231.
355. Provost, F.J. And Kolluri, V., A Survey of Methods for Scaling Up Inductive Learning Algorithms, Proc. 3Rd International Conference on Knowledge Discovery and Data Mining, 1997.
356. Provost, F., Jensen, D. And Oates, T., 1999, Efficient Progressive Sampling, In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp.23-32.
357. Quinlan, J.R. Learning efficient classification procedures and their application to chess endgames. R. Michalski, J. Carbonell, T. Mitchel. Machine learning: an AI approach. Los Altos, CA. Morgan Kaufman, 1983.
358. Quinlan, J.R., Induction of decision trees, Machine Learning 1, 81-106, 1986.
359. Quinlan, J.R., Simplifying decision trees, International Journal of Man-Machine Studies, 27, 221-234, 1987.
360. Quinlan, J.R., Decision Trees and Multivalued Attributes, J. Richards, ed., Machine Intelligence, V. 11, Oxford, England, Oxford Univ. Press, pp. 305-318, 1988.
361. Quinlan, J. R., Unknown attribute values in induction. In Segre, A. (Ed.), Proceedings of the Sixth International Machine Learning Workshop Cornell, New York. Morgan Kaufmann, 1989.
362. Quinlan, J. R., Unknown attribute values in induction. In Segre, A. (Ed.), Proceedings of the Sixth International Machine Learning Workshop Cornell, New York. Morgan Kaufmann, 1989.
363. Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, Los Altos, 1993.
364. Quinlan, J. R., Bagging, Boosting, and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 725-730, 1996.
365. Quinlan, J. R. And Rivest, R. L., Inferring Decision Trees Using The Minimum Description Length Principle. Information and Computation, 80:227-248, 1989.
366. Ragavan, H. And Rendell, L., Look ahead feature construction for learning hard concepts. In Proceedings of the Tenth International Machine Learning Conference: pp. 252-259, Morgan Kaufman, 1993.
367. Rahman, A. F. R., and Fairhurst, M. C. A new hybrid approach in combining multiple experts to recognize handwritten numerals. Pattern Recognition Letters, 18: 781-790, 1997.
368. Ramamurti, V., and Ghosh, J., Structurally Adaptive Modular Networks for Non-Stationary Environments, IEEE Transactions on Neural Networks, 10 (1):152-160, 1999.
369. Rand, W. M., Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66: 846-850, 1971.
370. Rao, R., Gordon, D., and Spears, W., For every generalization action, is there really an equal or opposite reaction? Analysis of conservation law. In Proc. Of the Twelveth International Conference on Machine Learning, pp. 471-479. Morgan Kaufmann, 1995.
371. Rastogi, R., and Shim, K., PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning, Data Mining and Knowledge Discovery, 4(4):315-344, 2000.
372. Ratsch G., Onoda T., and Muller K. R., Soft Margins for Adaboost, Machine Learning 42(3):287-320, 2001.
373. Ray, S., and Turi, R.H. Determination of Number of Clusters in K-Means Clustering and Application in Color Image Segmentation. Monash university, 1999.
374. R’enyi A., Probability Theory, North-Holland, Amsterdam, 1970
375. Buczak A. L. And Ziarko W., “Stages of The Discovery Process”, Klosgen W. And Zytkow J. M. (Eds.), Handbook of Data Mining and Knowledge Discovery, pages 185-192. Oxford University Press, 2002.
376. Ridgeway, G., Madigan, D., Richardson, T. And O’Kane, J. (1998), “Interpretable Boosted Naive Bayes Classification”, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp 101-104.
377. Rigoutsos I. And Floratos A., Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm, Bioinformatics, 14(2): page 229, 1998.
378. Rissanen, J., Stochastic complexity and statistical inquiry. World Scientific, 1989.
379. Rokach L., Averbuch M. And Maimon O., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 Springer-Verlag, 2004.
380. Rokach L., Chizi B., Maimon O., A Methodology for Improving the Performance of Non-ranker Feature Selection Filters, International Journal of Pattern Recognition and Artificial Intelligence, 21(5): 809-830, 2007.
381. Rokach L. And Maimon O., “Theory and Application of Attribute Decomposition”, Proceedings of the First IEEE International Conference on Data Mining, IEEE Computer Society Press, pp. 473-480, 2001
382. Rokach L. And Maimon O., Top Down Induction of Decision Trees Classifiers: A Survey, IEEE SMC Transactions Part C. Volume 35, Number 3, 2005a.
383. Rokach L. And Maimon O., Feature Set Decomposition for Decision Trees, Journal of Intelligent Data Analysis, Volume 9, Number 2, 2005b, pp 131-158.
384. Ronco, E., Gollee, H., and Gawthrop, P. J., Modular neural network and selfdecomposition. Csc Research Report CSC-96012, Centre for Systems and Control, University of Glasgow, 1996.
385. Rosen B. E., Ensemble Learning Using Decorrelated Neural Networks. Connect. Sci. 8(3): 373-384 (1996)
386. Rounds, E., A combined non-parametric approach to feature selection and binary decision tree design, Pattern Recognition 12, 313-317, 1980.
387. Rudin C., Daubechies I., and Schapire R. E., The Dynamics of Adaboost: Cyclic behavior and convergence of margins, Journal of Machine Learning Research Vol. 5, 1557-1595, 2004.
388. Rumelhart, D., G. Hinton and R. Williams, Learning internal representations through error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, D. Rumelhart and J. Mcclelland (eds.) Cambridge, MA: MIT Press., pp 2540, 1986.
389. Saaty, X., The analytic hierarchy process: A 1993 overview. Central European Journal for Operations Research and Economics, Vol. 2, No. 2, p. 119-137, 1993.
390. Safavin S. R. And Landgrebe, D., A survey of decision tree classifier methodology. Ieee Trans. On Systems, Man and Cybernetics, 21(3):660-674, 1991.
391. Sakar A., Mammone R.J., Growing and pruning neural tree networks, IEEE Trans. On Computers 42, 291-299, 1993.
392. Salzberg S. L., On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery, 1: 312-327, Kluwer Academic Publishers, Bosto, 1997.
393. Samuel, A., Some studies in machine learning using the game of checkers II: Recent progress. Ibm J. Res. Develop., 11:601-617, 1967.
394. Schaffer, C., When does overfitting decrease prediction accuracy in induced decision trees and rule sets? In Proceedings of the European Working Session on Learning (EWSL-91), pp. 192-205, Berlin, 1991.
395. Schaffer, C., Selecting a classification method by cross-validation. Machine Learning 13(1):135-143, 1993.
396. Schaffer J., A Conservation Law for Generalization Performance. In Proceedings of the 11th International Conference on Machine Learning: pp. 259-265, 1993.
397. Schapire, R.E., The strength of week learnability. In Machine learning 5(2), 197-227, 1990.
398. Schmitt, M., On the complexity of computing and learning with multiplicative neural networks, Neural Computation 14: 2, 241-301, 2002.
399. Schlimmer, J. C., Efficiently inducing determinations: A complete and systematic search algorithm that uses optimal pruning. In Proceedings of the 1993 International Conference on Machine Learning: pp 284-290, San Mateo, CA, Morgan Kaufmann, 1993.
400. Seewald, A.K. And Fiirnkranz, J., Grading classifiers, Austrian research institute for Artificial intelligence, 2001.
401. Selim, S.Z., and Ismail, M.A. K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. In IEEE transactions on pattern analysis and machine learning, vol. Pami-6, no. 1, January, 1984.
402. Selim, S. Z. And Al-Sultan, K. 1991. A simulated annealing algorithm for the clustering problem. Pattern Recogn. 24, 10 (1991), 1003-1008.
403. Selfridge, O. G. Pandemonium: a paradigm for learning. In Mechanization of Thought Processes: Proceedings of a Symposium Held at the National Physical Laboratory, November, 1958, 513-526. London: H.M.S.O., 1958.
404. Servedio, R., On Learning Monotone DNF under Product Distributions. Information and Computation 193, pp. 57-74, 2004.
405. Sethi, K., and Yoo, J. H., Design of multicategory, multifeature split decision trees using perceptron learning. Pattern Recognition, 27(7):939-947, 1994.
406. Shapiro, A. D. And Niblett, T., Automatic induction of classification rules for a chess endgame, in M. R. B. Clarke, ed., Advances in Computer Chess 3, Pergamon, Oxford, pp. 73-92, 1982.
407. Shapiro, A. D., Structured induction in expert systems, Turing Institute Press in association with Addison-Wesley Publishing Company, 1987.
408. Sharkey, A., On combining artificial neural nets, Connection Science, Vol. 8, pp.299-313, 1996.
409. Sharkey, A., Multi-Net Iystems, In Sharkey A. (Ed.) Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems. Pp. 1-30, SpringerVerlag, 1999.
410. Shafer, J. C., Agrawal, R. And Mehta, M., SPRINT: A Scalable Parallel Classifier for Data Mining, Proc. 22Nd Int. Conf. Very Large Databases, T. M. Vijayaraman and Alejandro P. Buchmann and C. Mohan and Nandlal L. Sarda (eds), 544-555, Morgan Kaufmann, 1996.
411. Shilen, S., Multiple binary tree classifiers. Pattern Recognition 23(7): 757-763, 1990.
412. Shilen, S., Nonparametric classification using matched binary decision trees. Pattern Recognition Letters 13: 83-87, 1992.
413. Sklansky, J. And Wassel, G. N., Pattern classifiers and trainable machines. Springerverlag, New York, 1981.
414. Skurichina M. And Duin R.P.W., Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications, 5(2):121-135, 2002
415. Smyth, P. And Goodman, R. (1991). Rule induction using information theory. Knowledge Discovery in Databases, AAAI/MIT Press.
416. Sneath, P., and Sokal, R. Numerical Taxonomy. W.H. Freeman Co., San Francisco, CA, 1973.
417. Snedecor, G. And Cochran, W. (1989). Statistical Methods. Owa State University Press, Ames, IA, 8th Edition.
418. Sohn S. Y., Choi, H., Ensemble based on Data Envelopment Analysis, ECML Meta Learning workshop, Sep. 4, 2001.
419. van Someren M., Torres C. And Verdenius F. (1997): A systematic Description of Greedy Optimisation Algorithms for Cost Sensitive Generalization. X. Liu, P.Cohen, M. Berthold (Eds.): “Advance in Intelligent Data Analysis” (IDA-97) LNCS 1280, pp. 247-257.
420. Sonquist, J. A., Baker E. L., and Morgan, J. N., Searching for Structure. Institute for Social Research, Univ. Of Michigan, Ann Arbor, MI, 1971.
421. Spirtes, P., Glymour C., and Scheines, R., Causation, Prediction, and Search. Springer Verlag, 1993.
422. Steuer R.E., Multiple Criteria Optimization: Theory, Computation and Application. John Wiley, New York, 1986.
423. Strehl A. And Ghosh J., Clustering Guidance and Quality Evaluation Using Relationship-based Visualization, Proceedings of Intelligent Engineering Systems Through Artificial Neural Networks, 5-8 November 2000, St. Louis, Missouri, USA, pp 483-488.
424. Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In Proc. Aaai Workshop on AI for Web Search, pp 58-64, 2000.
425. Tadepalli, P. And Russell, S., Learning from examples and membership queries with structured determinations, Machine Learning, 32(3), pp. 245-295, 1998.
426. Tan A. C., Gilbert D., Deville Y., Multi-class Protein Fold Classification using a New Ensemble Machine Learning Approach. Genome Informatics, 14:206-217, 2003.
427. Tani T. And Sakoda M., Fuzzy modeling by ID3 algorithm and its application to prediction of heater outlet temperature, Proc. Ieee Internat. Conf. On Fuzzy Systems, March 1992, pp. 923-930.
428. Taylor P. C., and Silverman, B. W., Block diagrams and splitting criteria for classification trees. Statistics and Computing, 3(4):147-161, 1993.
429. Tibshirani, R., Walther, G. And Hastie, T. (2000). Estimating the number of clusters in a dataset via the gap statistic. Tech. Rep. 208, Dept. Of Statistics, Stanford University.
430. Towell, G. Shavlik, J., Knowledge-based artificial neural networks, Artificial Intelligence, 70: 119-165, 1994.
431. Tresp, V. And Taniguchi, M. Combining estimators using non-constant weighting functions. In Tesauro, G., Touretzky, D., & Leen, T. (Eds.), Advances in Neural Information Processing Systems, volume 7: pp. 419-426, The MIT Press, 1995.
432. Tsallis C., Possible Generalization of Boltzmann-Gibbs Statistics, J. Stat. Phys., 52, 479-487, 1988.
433. Tsymbal A., and Puuronen S., Ensemble Feature Selection with the Simple Bayesian Classification in Medical Diagnostics, In: Proc. 15Thieee Symp. On Computer-Based Medical Systems CBMS2002, Maribor, Slovenia, IEEE CS Press, 2002, pp. 225-230.
434. Tsymbal A., and Puuronen S., and D. Patterson, Feature Selection for Ensembles of Simple Bayesian Classifiers, In: Foundations of Intelligent Systems: ISMIS2002, LNAI, Vol. 2366, Springer, 2002, pp. 592-600
435. Tsymbal A., Pechenizkiy M., Cunningham P., Diversity in search strategies for ensemble feature selection. Information Fusion 6(1): 83-98, 2005.
436. Tukey J.W., Exploratory data analysis, Addison-Wesley, Reading, Mass, 1977.
437. Tumer, K. And Ghosh J., Error Correlation and Error Reduction in Ensemble Classifiers, Connection Science, Special issue on combining artificial neural networks: ensemble approaches, 8 (3-4): 385-404, 1996.
438. Tumer, K., and Ghosh J., Linear and Order Statistics Combiners for Pattern Classification, in Combining Articial Neural Nets, A. Sharkey (Ed.), pp. 127-162, Springer-Verlag, 1999.
439. Tumer, K., and Ghosh J., Robust Order Statistics based Ensembles for Distributed Data Mining. In Kargupta, H. And Chan P., eds, Advances in Distributed and Parallel Knowledge Discovery, pp. 185-210, AAAI/MIT Press, 2000.
440. Turney P. (1995): Cost-Sensitive Classification: Empirical Evaluation of Hybrid, Genetic Decision Tree Induction Algorithm. Journal of Artificial Intelligence Research 2, pp. 369-409.
441. th ICML, Stanford, CA.
442. Tuv, E. And Torkkola, K., Feature filtering with ensembles using artificial contrasts. In Proceedings of the SIAM 2005 Int. Workshop on Feature Selection for Data Mining, Newport Beach, CA, 69-71, 2005.
443. Tyron R. C. And Bailey D.E. Cluster Analysis. Mcgraw-Hill, 1970.
444. Urquhart, R. Graph-theoretical clustering, based on limited neighborhood sets. Pattern recognition, vol. 15, pp. 173-187, 1982.
445. Utgoff, P. E., Perceptron trees: A case study in hybrid concept representations. Connection Science, 1(4):377-391, 1989.
446. Utgoff, P. E., Incremental induction of decision trees. Machine Learning, 4:161-186, 1989.
447. Utgoff, P. E., Decision tree induction based on efficient tree restructuring, Machine Learning 29 (1):5-44, 1997.
448. Utgoff, P. E., and Clouse, J. A., A Kolmogorov-Smirnoff Metric for Decision Tree Induction, Technical Report 96-3, University of Massachusetts, Department of Computer Science, Amherst, MA, 1996.
449. Vafaie, H. And De Jong, K. (1995). Genetic algorithms as a tool for restructuring feature space representations. In Proceedings of the International Conference on Tools with A. I. Ieee Computer Society Press.
450. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM 1984, pp. 1134-1142.
451. Van Rijsbergen, C. J., Information Retrieval. Butterworth, ISBN 0-408-70929-4, 1979.
452. Van Zant, P., Microchip fabrication: a practical guide to semiconductor processing, New York: McGraw-Hill, 1997.
453. Vapnik, V.N., The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
454. Veyssieres, M.P. And Plant, R.E. Identification of vegetation state-and-transition domains in California’s hardwood rangelands. University of California, 1998.
455. Wallace, C. S., MML Inference of Predictive Trees, Graphs and Nets. In A. Gam-merman (ed), Computational Learning and Probabilistic Reasoning, pp 43-66, Wiley, 1996.
456. Wallace, C. S., and Patrick J., Coding decision trees, Machine Learning 11: 7-22, 1993.
457. Wallace C. S. And Dowe D. L., Intrinsic classification by mml - the snob program. In Proceedings of the 7th Australian Joint Conference on Artificial Intelligence, pages 37-44, 1994.
458. Walsh P., Cunningham P., Rothenberg S., O’Doherty S., Hoey H., Healy R., An artificial neural network ensemble to predict disposition and length of stay in children presenting with bronchiolitis. European Journal of Emergency Medicine. 11(5):259-264, 2004.
459. Wan, W. And Perkowski, M. A., A new approach to the decomposition of incompletely specified functions based on graph-coloring and local transformations and its application to FPGAmapping, In Proc. Of the IEEE EURO-DAC’92, pp. 230-235, 1992.
460. Wang W., Jones P., Partridge D., Diversity between neural networks and decision trees for building multiple classifier systems, in: Proc. Int. Workshop on Multiple Classifier Systems (LNCS 1857), Springer, Calgiari, Italy, 2000, pp. 240-249.
461. Wang, X. And Yu, Q. Estimate the number of clusters in web documents via gap statistic. May 2001.
462. Ward, J. H. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58:236-244, 1963.
463. Warshall S., A theorem on Boolean matrices, Journal of the ACM 9, 1112, 1962.
464. Widmer, G. And Kubat, M., 1996, Learning in the Presence of Concept Drift and Hidden Contexts, Machine Learning 23(1), pp. 69101.
465. Webb G., MultiBoosting: A technique for combining boosting and wagging. Machine Learning, 40(2): 159-196, 2000.
466. Webb G., and Zheng Z., Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques. Ieee Transactions on Knowledge and Data Engineering, 16 No. 8:980-991, 2004.
467. Weigend, A. S., Mangeas, M., and Srivastava, A. N. Nonlinear gated experts for time-series - discovering regimes and avoiding overfitting. International Journal of Neural Systems 6(5):373-399, 1995.
468. Wolf L., Shashua A., Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach, Journal of Machine Learning Research, Vol 6, pp. 1855-1887, 2005.
469. Wolpert, D.H., Stacked Generalization, Neural Networks, Vol. 5, pp. 241-259, Pergamon Press, 1992.
470. Wolpert, D. H., The relationship between PAC, the statistical physics framework, the Bayesian framework, and the VC framework. In D. H. Wolpert, editor, The Mathematics of Generalization, The SFI Studies in the Sciences of Complexity, pages 117-214. Addison Wesley, 1995.
471. Wolpert, D. H., “The lack of a priori distinctions between learning algorithms, ” Neural Computation 8: 1341-1390, 1996.
472. Woods K., Kegelmeyer W., Bowyer K., Combination of multiple classifiers using local accuracy estimates, IEEE Transactions on Pattern Analysis and Machine Intelligence 19:405-410, 1997.
473. Wyse, N., Dubes, R. And Jain, A.K., A critical evaluation of intrinsic dimensionality algorithms, Pattern Recognition in Practice, E.S. Gelsema and L.N. Kanal (eds.), North-Holland, pp. 415-425, 1980.
474. Yuan Y., Shaw M., Induction of fuzzy decision trees, Fuzzy Sets and Systems 69(1995):125-139.
475. Zahn, C. T., Graph-theoretical methods for detecting and describing gestalt clusters. Ieee trans. Comput. C-20 (Apr.), 68-86, 1971.
476. Zaki, M. J., Ho C. T., and Agrawal, R., Scalable parallel classification for data mining on shared- memory multiprocessors, in Proc. Ieee Int. Conf. Data Eng., Sydney, Australia, WKDD99, pp. 198-205, 1999.
477. Zaki, M. J., Ho C. T., Eds., Large- Scale Parallel Data Mining. New York: Springer- Verlag, 2000.
478. Zantema, H., and Bodlaender H. L., Finding Small Equivalent Decision Trees is Hard, International Journal of Foundations of Computer Science, 11(2):343-354, 2000.
479. Zadrozny B. And Elkan C. (2001): Learning and Making Decisions When Costs and Probabilities are Both Unknown. In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining (KDD’01).
480. Zeira, G., Maimon, O., Last, M. And Rokach, L., Change detection in classification models of data mining, Data Mining in Time Series Databases. World Scientific Publishing, 2003.
481. Zenobi, G., and Cunningham, P. Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. In Proceedings of the European Conference on Machine Learning, 2001.
482. Zhou Z., Chen C., Hybrid decision tree, Knowledge-Based Systems 15, 515-528, 2002.
483. Zhou Z., Jiang Y., NeC4.5: Neural Ensemble Based C4.5, IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 770-773, Jun., 2004.
484. th International Conference, RSFDGrC, Chongqing, China, Proceedings. Lecture Notes in Computer Science 2639, pp.476-483, 2003.
485. Zhou, Z. H., Wu J., Tang W., Ensembling neural networks: many could be better than all. Artificial Intelligence 137: 239-263, 2002.
486. Zimmermann H. J., Fuzzy Set Theory and its Applications, Springer, 4th edition, 2005.
487. Zupan, B., Bohanec, M., Demsar J., and Bratko, I., Feature transformation by function decomposition, IEEE intelligent systems & their applications, 13: 38-43, 1998.
488. Zupan, B., Bratko, I., Bohanec, M. And Demsar, J., 2000, Induction of concept hierarchies from noisy data, in Proceedings of the Seventeenth International Conference on Machine Learning (ICML-2000), San Francisco, CA, pp. 1199-1206.