Ensemble methods: Foundations and algorithms

Ensemble Methods: Foundations and Algorithms

Volumn , Issue , 2012, Pages 1-218

  Zhou, Zhi Hua ^a  

^a NONE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85055384819     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b12207     Document Type: Book

References (429)

1. N. Abe, and H. Mamitsuka. Query learning strategies using Boosting, and Bagging. In Proceedings of the 15th International Conference onMachine Learning, pages 1-9, Madison, WI, 1998.
2. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for datamining applications. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 94-105, Seattle, WA, 1998.
3. M. R. Ahmadzadeh, and M. Petrou. Use of Dempster-Shafer theory to combine classifiers which use different class boundaries. Pattern Analysis, and Application, 6(1): 41-46, 2003.
4. A. Al-Ani, and M. Deriche. A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence. Journal of Artificial Intelligence Research, 17(1): 333-361, 2002.
5. K. M. Ali, and M. J. Pazzani. Error reduction through learning multiple descriptions. Machine Learning, 24(3): 173-202, 1996.
6. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1: 113-141, 2000.
7. E. Alpaydin. Introduction to Machine Learning. MITPress, Cambridge, MA, 2nd edition, 2010.
8. M. R. Anderberg. Cluster Analysis for Applications. Academic, NewYork, NY, 1973.
9. R. Andrews, J. Diederich, and A. B. Tickle. Survey, and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, 8(6): 373-389, 1995.
10. M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: Ordering points to identify the clustering structure. In Proceedings of the ACM SIGMOD International Conference onManagement of Data, pages 49-60, Philadelphia, PA, 1999.
11. M. Anthony, and N. Biggs. Computational Learning Theory. Cambridge University Press, Cambridge, UK, 1992.
12. M. B. Araújo, and M. New. Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22(1): 42-47, 2007.
13. J. A. Aslam, and S. E. Decatur. General bounds on statistical query learning, and PAC learning with noise via hypothesis boosting. In Proceedings of the 35th IEEE Annual Symposium on Foundations of Computer Science, pages 282-291, Palo Alto, CA, 1993.
14. E. Asmis. Epicurus’ Scientific Method. Cornell University Press, Ithaca, NY, 1984.
15. A. V. Assche, and H. Blockeel. Seeing the forest through the trees: Learning a comprehensible model froman ensemble. In Proceedings of the 18th European Conference onMachine Learning, pages 418-429, Warsaw, Poland, 2007.
16. S. Avidan. Ensemble tracking. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 29(2): 261-271, 2007.
17. R. Avogadri, and G. Valentini. Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artificial Intelligence in Medicine, 45(2-3): 173-183, 2009.
18. H. Ayad, and M. Kamel. Finding natural clusters usingmulti-clusterer combiner based on shared nearest neighbors. In Proceedings of the 4th InternationalWorkshop on Multiple Classifier Systems, pages 166-175, Surrey, UK, 2003.
19. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the 21st International Conference onMachine Learning, Banff, Canada, 2004.
20. B. Bakker, and T. Heskes. Clustering ensembles of neural network models. Neural Networks, 16(2): 261-269, 2003.
21. M.-F. Balcan, A. Z. Broder, and T. Zhang. Margin based active learning. In Proceedings of the 20th Annual Conference on Learning Theory, pages 35-50, San Diego, CA, 2007.
22. R. E. Banfield, L. O. Hall, K. W. Bowyer, and W. P. Kegelmeyer. Ensemble diversitymeasures, and their application to thinning. Information Fusion, 6(1): 49-62, 2005.
23. E. Bauer, and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2): 105-139, 1999.
24. K. Bennett, A. Demiriz, and R. Maclin. Exploiting unlabeled data in ensemble methods. In Proceedings of the 8th ACM SIGKDD International Conference on KnowledgeDiscovery, and DataMining, pages 289-296, Edmonton, Canada, 2002.
25. J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. Kégl. Aggregate features, and AdaBoost for music classification. Machine Learning, 65(2-3): 473-484, 2006.
26. Y. Bi, J. Guan, and D. Bell. The combination of multiple classifiers using an evidential reasoning approach. Artificial Intelligence, 172(15): 1731-1751, 2008.
27. P. J. Bickel, Y. Ritov, and A. Zakai. Some theory for generalized boosting algorithms. Journal ofMachine Learning Research, 7: 705-732, 2006.
28. J. A. Bilmes. A gentle tutorial of the EM algorithm, and its applications to parameter estimation for Gaussian mixture, and hidden Markov models. Technical Report TR-97-021, Department of Electrical Engineering, and Computer Science, University of California, Berkeley, CA, 1998.
29. C. M. Bishop. Neural Networks for Pattern Recognition. OxfordUniversity Press, New York, NY, 1995.
30. C. M. Bishop. Pattern Recognition, and Machine Learning. Springer, New York, NY, 2006.
31. C. M. Bishop, and M. Svensén. Bayesian hierarchical mixtures of experts. In Proceedings of the 19th Conference inUncertainty in Artificial Intelligence, pages 57-64, Acapulco, Mexico, 2003.
32. A. Blum, and T. Mitchell. Combining labeled, and unlabeled data with cotraining. In Proceedings of the 11th Annual Conference on Computational Learning Theory, pages 92-100, Madison, WI, 1998.
33. J. K. Bradley, and R. E. Schapire. FilterBoost: Regression, and classification on large datasets. In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 185-192. MIT Press, Cambridge, MA, 2008.
34. U. Brefeld, and T. Scheffer. AUCmaximizing support vector learning. In Proceedings of the ICML 2005 Workshop on ROC Analysis in Machine Learning, Bonn, Germany, 2005.
35. U. Brefeld, P. Geibel, and F. Wysotzki. Support vector machines with example dependent costs. In Proceedings of the 14th European Conference on Machine Learning, pages 23-34, Cavtat-Dubrovnik, Croatia, 2003.
36. L. Breiman. Bias, variance, and arcing classifiers. Technical Report 460, Statistics Department, University of California, Berkeley, CA, 1996a.
37. L. Breiman. Stacked regressions. Machine Learning, 24(1): 49-64, 1996b.
38. L. Breiman. Out-of-bag estimation. Technical report, Department of Statistics, University of California, 1996c.
39. L. Breiman. Bagging predictors. Machine Learning, 24(2): 123-140, 1996d.
40. L. Breiman. Prediction games, and arcing algorithms. Neural Computation, 11(7): 1493-1517, 1999.
41. L. Breiman. Randomizing outputs to increase prediction accuracy. Machine Learning, 40(3): 113-120, 2000.
42. L. Breiman. Random forests. Machine Learning, 45(1): 5-32, 2001.
43. L. Breiman. Population theory for boosting ensembles. Annals of Statistics, 32(1): 1-11, 2004.
44. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen. Classification, and Regression Trees. Chapman, and Hall/CRC, Boca Raton, FL, 1984.
45. G. Brown. An information theoretic perspective on multiple classifier systems. In Proceedings of the 8th InternationalWorkshop onMultiple Classifier Systems, pages 344-353, Reykjavik, Iceland, 2009.
46. G. Brown. Some thoughts at the interface of ensemble methods, and feature selection. Keynote at the 9th International Workshop on Multiple Classifier Systems, Cairo, Egypt, 2010.
47. G. Brown, J. L. Wyatt, R. Harris, and X. Yao. Diversity creation methods: A survey, and categorisation. Information Fusion, 6(1): 5-20, 2005a.
48. G. Brown, J. L. Wyatt, and P. Tino. Managing diversity in regression ensembles. Journal ofMachine Learning Research, 6: 1621-1650, 2005b.
49. P. Bühlmann, and B. Yu. Analyzing bagging. Annals of Statistics, 30(4): 927-961, 2002.
50. P. Bühlmann, and B. Yu. Boosting with the l2 loss: Regression, and classification. Journal of the American Statistical Association, 98(462): 324-339, 2003.
51. A. Buja, and W. Stuetzle. The effect of bagging on variance, bias, and mean squared error. Technical report, AT&T Labs-Research, 2000a.
52. A. Buja, and W. Stuetzle. Smoothing effects of bagging. Technical report, AT&T Labs-Research, 2000b.
53. A. Buja, and W. Stuetzle. Observations on bagging. Statistica Sinica, 16(2): 323-351, 2006.
54. R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes. Ensemble selection from libraries of models. In Proceedings of the 21st International Conference onMachine Learning, pages 18-23, Banff, Canada, 2004.
55. P. D. Castro, G. P. Coelho, M. F. Caetano, and F. J. V. Zuben. Designing ensembles of fuzzy classification systems: An immune-inspired approach. In Proceedings of the 4th International Conference on Artificial Immune Systems, pages 469-482, Banff, Canada, 2005.
56. P. Chan, and S. Stolfo. Toward scalable learning with non-uniform class, and cost distributions: A case study in credit card fraud detection. In Proceeding of the 4th International Conference on Knowledge Discovery, and Data Mining, pages 164-168, New York, NY, 1998.
57. P. K. Chan, W. Fan, A. L. Prodromidis, and S. J. Stolfo. Distributed datamining in credit card fraud detection. IEEE Intelligent Systems, 14(6): 67-74, 1999.
58. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Computing Surveys, 41(3): 1-58, 2009.
59. O. Chapelle, and A. Zien. Semi-supervised learning by low density separation. In Proceedings of the 10th International Workshop on Artificial Intelligence, and Statistics, pages 57-64. Barbados, 2005.
60. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
61. N. V. Chawla. Datamining for imbalanced datasets: An overview. In O. Maimon, and L. Rokach, editors, The Data Mining, and Knowledge Discovery Handbook, pages 853-867. Springer, New York, NY, 2006.
62. N. V. Chawla, K.W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority oversampling technique. Journal of Artificial Intelligence Research, 16: 321-357, 2002.
63. N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer. SMOTEBoost: Improving prediction of theminority class in boosting. In Proceedings of the 7th European Conference on Principles, and Practice of Knowledge Discovery in Databases, pages 107-119, Cavtat-Dubrovnik, Croatia, 2003.
64. H. Chen, P.Tiño, and X. Yao. A probabilistic ensemble pruning algorithm. In Working Notes of ICDM’06 Workshop on Optimization-Based Data Mining Techniques with Applications, pages 878-882, Hong Kong, China, 2006.
65. H. Chen, P. Tiňo, and X. Yao. Predictive ensemble pruning by expectation propagation. IEEE Transactions on Knowledge, and Data Engineering, 21(7): 999-1013, 2009.
66. B. Clarke. Comparing Bayes model averaging, and stacking when model approximation error cannot be ignored. Journal ofMachine Learning Research, 4: 683-712, 2003.
67. A. L. V. Coelho, C. A. M. Lima, and F. J. V. Zuben. GA-based selection of components for heterogeneous ensembles of support vector machines. In Proceedings of the Congress on Evolutionary Computation, pages 2238-2244, Canberra, Australia, 2003.
68. J. Cohen. A coefficient of agreement for nominal scales. Educational, and PsychologicalMeasurement, 20(1): 37-46, 1960.
69. I. Corona, G. Giacinto, C. Mazzariello, F. Roli, and C. Sansone. Information fusion for computer security: State of the art, and open issues. Information Fusion, 10(4): 274-284, 2009.
70. T. M. Cover, and P. E. Hart. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1): 21-27, 1967.
71. T. M. Cover, and J. A. Thomas. Elements of Information Theory. Wiley, New York, NY, 1991.
72. M. Coyle, and B. Smyth. On the use of selective ensembles for relevance classification in case-based web search. In Proceedings of the 8th European Conference on Case-Based Reasoning, pages 370-384, Fethiye, Turkey, 2006.
73. K. Crammer, and Y. Singer. On the learnability, and design of output codes for multiclass problems. Machine Learning, 47(2-3): 201-233, 2002.
74. N. Cristianini, and J. Shawe-Taylor. An Introduction to Support Vector Machines, and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK, 2000.
75. P. Cunningham, and J. Carney. Diversity versus quality in classification ensembles based on feature selection. Technical Report TCD-CS-2000-02, Department of Computer Science, Trinity College Dublin, 2000.
76. A. Cutler, and G. Zhao. PERT - perfect random tree ensembles. In Proceedings of the 33rd Symposium on the Interface of Computing Science, and Statistics, pages 490-497, CostaMesa, CA, 2001.
77. I. Dagan, and S. P. Engelson. Committee-based sampling for training probabilistic classifiers. In Proceedings of the 12th International Conference on Machine Learning, pages 150-157, San Francisco, CA, 1995.
78. F. d’Alché-Buc, Y. Grandvalet, and C. Ambroise. Semi-supervised marginboost. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 553-560. MIT Press, Cambridge, MA, 2002.
79. B. V. Dasarathy, editor. Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA, 1991.
80. S. Dasgupta. Analysis of a greedy active learning strategy. In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 337-344. MIT Press, Cambridge, MA, 2005.
81. S. Dasgupta. Coarse sample complexity bounds for active learning. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 235-242. MIT Press, Cambridge, MA, 2006.
82. S. Dasgupta, and D. Hsu. Hierarchical sampling for active learning. In Proceedings of the 25th International Conference onMachine Learning, pages 208-215, Helsinki, Finland, 2008.
83. S. Dasgupta, A. T. Kalai, and C. Monteleoni. Analysis of perceptron-based active learning. In Proceedings of the 18th Annual Conference on Learning Theory, pages 249-263, Bertinoro, Italy, 2005.
84. J. Davis, and M. Goadrich. The relationship between precision-recall, and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning, pages 233-240, Pittsburgh, PA, 2006.
85. W. H. E. Day, and H. Edelsbrunner. Efficient algorithms for agglomerative hierarchical clustering methods. Journal of Classification, 1: 7-24, 1984.
86. N. C. de Concorcet. Essai sur l’Application de l’Analyze à la Probabilité des Décisions Rendues à la Pluralité des Voix. Imprimérie Royale, Paris, France, 1785.
87. A. Demiriz, K. P. Bennett, and J. Shawe-Taylor. Linear programming boosting via column generation. Machine Learning, 46(1-3): 225-254, 2002.
88. A. P. Dempster. Upper, and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38(2): 325-339, 1967.
89. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Soceity, Series B, 39(1): 1-38, 1977.
90. J. Demšar. Statistical comparisons of classifiers over multiple data sets. Journal ofMachine Learning Research, 7: 1-30, 2006.
91. L. Didaci, G. Giacinto, F. Roli, and G. L. Marcialis. A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognition, 38(11): 2188-2191, 2005.
92. T. G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7): 1895-1923, 1998.
93. T. G. Dietterich. Ensemble methods in machine learning. In Proceedings of the 1st InternationalWorkshop onMultiple Classifier Systems, pages 1-15, Sardinia, Italy, 2000a.
94. T. G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2): 139-157, 2000b.
95. T. G. Dietterich, and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2: 263-286, 1995.
96. T.G. Dietterich, G. Hao, and A. Ashenfelter. Gradient tree boosting for training conditional random fields. Journal of Machine Learning Research, 9: 2113-2139, 2008.
97. C. Domingo, and O. Watanabe. Madaboost: A modification of AdaBoost. In Proceedings of the 13th Annual Conference on Computational Learning Theory, pages 180-189, Palo Alto, CA, 2000.
98. P. Domingos. Knowledge discovery via multiple models. Intelligent Data Analysis, 2(1-4): 187-202, 1998.
99. P. Domingos. MetaCost: A general method for making classifiers costsensitive. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery, and DataMining, pages 155-164, SanDiego, CA, 1999.
100. P. Domingos, and M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2-3): 103-137, 1997.
101. C. Drummond, and R. C. Holte. Exploiting the cost of (in)sensitivity of decision tree splitting criteria. In Proceedings of the 17th International Conference onMachine Learning, pages 239-246, San Francisco, CA, 2000.
102. C. Drummond, and R. C. Holte. Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1): 95-130, 2006.
103. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley, New York, NY, 2nd edition, 2000.
104. B. Efron, and R. Tibshirani. An Introduction to the Bootstrap. Chapman& Hall, New York, NY, 1993.
105. C. Elkan. The foundations of cost-sensitive learning. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, pages 973-978, Seattle, WA, 2001.
106. S. Escalera, O. Pujol, and P. Radeva. Boosted landmarks of contextual descriptors, and Forest-ECOC: A novel framework to detect, and classify objects in clutter scenes. Pattern Recognition Letters, 28(13): 1759-1768, 2007.
107. S. Escalera, O. Pujol, and P. Radeva. Error-correcting ouput codes library. Journal ofMachine Learning Research, 11: 661-664, 2010a.
108. S. Escalera, O. Pujol, and P. Radeva. On the decoding process in ternary error-correcting output codes. IEEE Transaction on Pattern Analysis, and Machine Intelligence, 32(1): 120-134, 2010b.
109. A. Estabrooks, T. Jo, and N. Japkowicz. A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1): 18-36, 2004.
110. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases. In Proceedings of the 2nd International Conference on Knowledge Discovery, and Data Mining, pages 226-231, Portland, OR, 1996.
111. V. Estivill-Castro. Why so many clustering algorithms - A position paper. SIGKDD Explorations, 4(1): 65-75, 2002.
112. W. Fan. On the optimality of probability estimation by random decision trees. In Proceedings of the 19th National Conference on Artificial Intelligence, pages 336-341, San Jose, CA, 2004.
113. W. Fan, S. J. Stolfo, J. Zhang, and P. K. Chan. AdaCost: Misclassification costsensitive boosting. In Proceedings of the 16th International Conference on Machine Learning, pages 97-105, Bled, Slovenia, 1999.
114. W. Fan, F. Chu, H. Wang, and P. S. Yu. Pruning, and dynamic scheduling of cost-sensitive ensembles. In Proceedings of the 18th National Conference on Artificial Intelligence, pages 146-151, Edmonton, Canada, 2002.
115. W. Fan, H. Wang, P. S. Yu, and S. Ma. Is random model better? On its accuracy, and efficiency. In Proceedings of the 3rd IEEE International Conferenceon Data Mining, pages 51-58, Melbourne, FL, 2003.
116. R. Fano. Transmission of Information: Statistical Theory of Communications. MIT Press, Cambridge, MA, 1961.
117. T. Fawcett. ROC graphs with instance varying costs. Pattern Recognition Letters, 27(8): 882-891, 2006.
118. X. Z. Fern, and C. E. Brodley. Random projection for high dimensional data clustering: A cluster ensemble approach. In Proceedings of the 20th International Conference on Machine Learning, pages 186-193, Washington, DC, 2003.
119. X. Z. Fern, and C. E. Brodley. Solving cluster ensemble problems by bipartite graph partitioning. In Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004.
120. X. Z. Fern, and W. Lin. Cluster ensemble selection. In Proceedings of the 8th SIAM International Conference on Data Mining, pages 787-797, Atlanta, GA, 2008.
121. C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana. From ensemble methods to comprehensible models. In Proceedings of the 5th International Conference onDiscovery Science, pages 165-177, Lübeck, Germany, 2002.
122. D. Fisher. Improving inference through conceptual clustering. In Proceedings of the 6th National Conference on Artificial Intelligence, pages 461-465, Seattle, WA, 1987.
123. J. L. Fleiss. StatisticalMethods for Rates, and Proportions. JohnWiley & Sons, New York, NY, 2nd edition, 1981.
124. E. Frank, and M. Hall. Visualizing class probability estimators. In Proceedings of the 7th European Conference on Principles, and Practice of Knowledge Discovery in Databases, pages 168-179, Cavtat-Dubrovnik, Croatia, 2003.
125. A. Fred, and A. K. Jain. Data clustering using evidence accumulation. In Proceedings of the 16th International Conference on Pattern Recognition, pages 276-280, Quebec, Canada, 2002.
126. A. Fred, and A. K. Jain. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 27(6): 835-850, 2005.
127. Y. Freund. Boosting a weak learning algorithm by majority. Information, and Computation, 121(2): 256-285, 1995.
128. Y. Freund. An adaptive version of the boost bymajority algorithm. Machine Learning, 43(3): 293-318, 2001.
129. Y. Freund. A more robust boosting algorithm. CORR abs/0905.2138, 2009.
130. Y. Freund, and R. E. Schapire. A decision-theoretic generalization of online learning, and an application to boosting. In Proceedings of the 2nd European Conference on Computational Learning Theory, pages 23-37, Barcelona, Spain, 1995.
131. Y. Freund, and R. E. Schapire. A decision-theoretic generalization of on-line learning, and an application to boosting. Journal of Computer, and System Sciences, 55(1): 119-139, 1997.
132. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3): 133-168, 1997.
133. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting (with discussions). Annals of Statistics, 28(2): 337-407, 2000.
134. J. H. Friedman, and P. Hall. On bagging, and nonlinear estimation. Journal of Statistical Planning, and Inference, 137(3): 669-683, 2007.
135. J. H. Friedman, and W. Stuetzle. Projection pursuit regression. Journal of American Statistical Association, 76(376): 817-823, 1981.
136. N. Friedman, D. Geiger, and M. Goldszmidt. Bayesian network classifiers. Machine Learning, 29(2): 131-163, 1997.
137. G. Fumera, and F. Roli. A theoretical, and experimental analysis of linear combiners for multiple classifier systems. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 27(6): 942-956, 2005.
138. W. Gao, and Z.-H. Zhou. Approximation stability, and boosting. In Proceedings of the 21st International Conference on Algorithmic Learning Theory, pages 59-73, Canberra, Australia, 2010a.
139. W. Gao, and Z.-H. Zhou. On the doubt about margin explanation of boosting. CORR abs/1009.3613, 2012.
140. C. W. Gardiner. Handbook of Stochastic Methods. Springer, NewYork, NY, 3rd edition, 2004.
141. S. Geman, E. Bienenstock, and R. Doursat. Neural networks, and the bias/variance dilemma. Neural Computation, 4(1): 1-58, 1992.
142. P. Geurts, D. Ernst, and L. Wehenkel. Extremely randomized trees. Machine Learning, 63(1): 3-42, 2006.
143. G. Giacinto, and F. Roli. Adaptive selection of image classifiers. In Proceedings of the 9th International Conference on Image Analysis, and Processing, pages 38-45, Florence, Italy, 1997.
144. G. Giacinto, and F. Roli. A theoretical framework for dynamic classifier selection. In Proceedings of the 15th International Conference on Pattern Recognition, pages 2008-2011, Barcelona, Spain, 2000a.
145. G. Giacinto, and F. Roli. Dynamic classifier selection. In Proceedings of the 1st InternationalWorkshop onMultiple Classifier Systems, pages 177-189, Cagliari, Italy, 2000b.
146. G. Giacinto, and F. Roli. Design of effective neural network ensembles for image classification purposes. Image, and Vision Computing, 19(9-10): 699-707, 2001.
147. G. Giacinto, F. Roli, and G. Fumera. Design of effective multiple classifier systems by clustering of classifiers. In Proceedings of the 15th International Conference on Pattern Recognition, pages 160-163, Barcelona, Spain, 2000.
148. G. Giacinto, F. Roli, and L. Didaci. Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Letters, 24(12): 1795-1803, 2003.
149. G. Giacinto, R. Perdisci, M. D. Rio, and F. Roli. Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Information Fusion, 9(1): 69-82, 2008.
150. T. Gneiting, and A. E. Raftery. Atmospheric science: Weather forecasting with ensemble methods. Science, 310(5746): 248-249, 2005.
151. K. Goebel, M. Krok, and H. Sutherland. Diagnostic information fusion: Requirements flowdown, and interface issues. In Proceedings of the IEEE Aerospace Conference, volume 6, pages 155-162, Big Sky, MT, 2000.
152. D. E. Goldberg. Genetic Algorithm in Search, Optimization, and Machine Learning. Addison-Wesley, Boston, MA, 1989.
153. D. M. Green, and J. M. Swets. Signal Detection Theory, and Psychophysics. John Wiley & Sons, New York, NY, 1966.
154. A. J. Grove, and D. Schuurmans. Boosting in the limit: Maximizing the margin of learned ensembles. In Proceedings of the 15th National Conference on Artificial Intelligence, pages 692-699, Madison, WI, 1998.
155. S. Guha, R. Rastogi, and K. Shim. ROCK: A robust clustering algorithm for categorical attributes. In Proceedings of the 15th International Conference on Data Engineering, pages 512-521, Sydney, Australia, 1999.
156. H. Guo, and H. L. Viktor. Learning from imbalanced data sets with boosting, and data generation: The DataBoost-IM approach. SIGKDD Explorations, 6(1): 30-39, 2004.
157. Y. Guo, and D. Schuurmans. Discriminative batch mode active learning. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 593-600. MIT Press, Cambridge, MA, 2008.
158. I. Guyon, and A. Elisseeff. An introduction to variable, and feature selection. Journal ofMachine Learning Research, 3: 1157-1182, 2003.
159. S. T. Hadjitodorov, and L. I. Kuncheva. Selecting diversifying heuristics for cluster ensembles. In Proceedings of the 7th International Workshop on Multiple Classifier Systems, pages 200-209, Prague, Czech, 2007.
160. S. T. Hadjitodorov, L. I. Kuncheva, and L. P. Todorova. Moderate diversity for better cluster ensembles. Information Fusion, 7(3): 264-275, 2006.
161. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. Journal of Intelligent Information Systems, 17(2-3): 107-145, 2001.
162. J. Han, and M. Kamber. Data Mining: Concepts, and Techniques. Morgan Kaufmann, San Francisco, CA, 2nd edition, 2006.
163. D. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. MITPress, Cambridge, MA, 2001.
164. D. J. Hand. Measuring classifier performance: A coherent alternative to the area under the ROC curve. Machine Learning, 77(1): 103-123, 2009.
165. D. J. Hand, and R. J. Till. A simple generalization of the area under the ROC curve to multiple classification problems. Machine Learning, 45(2): 171-186, 2001.
166. J. A. Hanley, and B. J. McNeil. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148(3): 839-843, 1983.
167. L. K. Hansen, and P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 12(10): 993-1001, 1990.
168. M. Harries. Boosting a strong learner: Evidence against theminimummargin. In Proceedings of the 16th International Conference on Machine Learning, pages 171-179, Bled, Slovenia, 1999.
169. T. Hastie, and R. Tibshirani. Classification by pairwise coupling. Annals of Statistics, 26(2): 451-471, 1998.
170. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, New York, NY, 2001.
171. S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice-Hall, Upper Saddle River, NJ, 2nd edition, 1998.
172. H. He, and E. A. Garcia. Learning from imbalanced data. IEEE Transactions on Knowledge, and Data Engineering, 21(9): 1263-1284, 2009.
173. Z. He, X. Xu, and S. Deng. A cluster ensemble method for clustering categorical data. Information Fusion, 6(2): 143-151, 2005.
174. M. Hellman, and J. Raviv. Probability of error, equivocation, and the Chernoff bound. IEEE Transactions on Information Theory, 16(4): 368-372, 1970.
175. D. Hernández-Lobato, G. Martínez-Muñoz, and A. Suárez. Statistical instance-based pruning in ensembles of independent classifiers. IEEE Transaction on Pattern Analysis, and Machine Intelligence, 31(2): 364-369, 2009.
176. D. Hernández-Lobato, G. Martínez-Muñoz, and A. Suárez. Empirical analysis, and evaluation of approximate techniques for pruning regression bagging ensembles. Neurocomputing, 74(12-13): 2250-2264, 2011.
177. A. Hinneburg, and D. A. Keim. An efficient approach to clustering in large multimedia databases with noise. In Proceedings of the 4th International Conference on Knowledge Discovery, and Data Mining, pages 58-65, New York, NY, 1998.
178. T. K. Ho. Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis, and Recognition, pages 278-282, Montreal, Canada, 1995.
179. T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 20(8): 832-844, 1998.
180. T. K. Ho, J. J. Hull, and S. N. Srihari. Decision combination inmultiple classifier systems. IEEE Transaction on Pattern Analysis, and Machine Intelligence, 16(1): 66-75, 1994.
181. V. Hodge, and J. Austin. A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2): 85-126, 2004.
182. S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu. Semisupervised SVMbatchmode active learning with applications to image retrieval. ACMTransactions on Information Systems, 27(3): 1-29, 2009.
183. Y. Hong, S. Kwong, H. Wang, and Q. Ren. Resampling-based selective clustering ensembles. Pattern Recognition Letters, 41(9): 2742-2756, 2009.
184. P. Hore, L. Hall, and D. Goldgof. Acluster ensembleframework for large data sets. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pages 3342-3347, Taipei, Taiwan, ROC, 2006.
185. P. Hore, L. O. Hall, and D. B. Goldgof. A scalable framework for cluster ensembles. Pattern Recognition, 42(5): 676-688, 2009.
186. C.-W. Hsu, and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2): 415-425, 2002.
187. X. Hu, E. K. Park, and X. Zhang. Microarray gene cluster identification, and annotation through cluster ensemble, and EM-based informative textual summarization. IEEE Transactions on Information Technology in Biomedicine, 13(5): 832-840, 2009.
188. F.-J. Huang, Z.-H. Zhou, H.-J. Zhang, and T. Chen. Pose invariant face recognition. In Proceedings of the 4th IEEE International Conference on Automatic Face, and Gesture Recognition, pages 245-250, Grenoble, France, 2000.
189. S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative, and representative examples. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems 23, pages 892-900. MIT Press, Cambridge, MA, 2010.
190. Y. S. Huang, and C. Y. Suen. A method of combiningmultiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 17(1): 90-94, 1995.
191. Z. Huang. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining, and Knowledge Discovery, 2(3): 283-304, 1998.
192. R. A. Hutchinson, L.-P. Liu, and T. G. Dietterich. Incorporating boosted regression trees into ecological latent variable models. In Proceedings of the 25th AAAI Conference on Artificial Intelligence, pages 1343-1348, San Francisco, CA, 2011.
193. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptivemixtures of local experts. Neural Computation, 3(1): 79-87, 1991.
194. A. K. Jain, and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, Upper Saddle River, NJ, 1988.
195. A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3): 264-323, 1999.
196. G. M. James. Variance, and bias for general loss functions. Machine Learning, 51(2): 115-135, 2003.
197. T. Joachims. Transductive inference for text classification using support vector machines. In Proceedings of the 16th International Conference on Machine Learning, pages 200-209, Bled, Slovenia, 1999.
198. T. Joachims. A support vector method for multivariate performance measures. In Proceedings of the 22nd International Conference on Machine Learning, pages 384-391, Bonn, Germany, 2005.
199. I. T. Jolliffe. Principal Component Analysis. Springer, NewYork, NY, 2nd edition, 2002.
200. M. I. Jordan, and R. A. Jacobs. Hierarchies of adaptive experts. In J. E. Moody, S. J. Hanson, and R. Lippmann, editors, Advances in Neural Information Processing Systems 4, pages 985-992. Morgan Kaufmann, San Francisco, CA, 1992.
201. M. I. Jordan, and L. Xu. Convergence results for theEMapproach tomixtures of experts architectures. Neural Networks, 8(9): 1409-1431, 1995.
202. G. Karypis, and V. Kumar. A fast, and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20(1): 359-392, 1998.
203. G. Karypis, R. Aggarwal, V. Kumar, and S. Shekhar. Multilevel hypergraph partitioning: Application in VLSI domain. In Proceedings of the 34th Annual Design Automation Conference, pages 526-529, Anaheim, CA, 1997.
204. L. Kaufman, and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. JohnWiley & Sons, New York, NY, 1990.
205. M. Kearns. Efficient noise tolerant learning fromstatistical queries. Journal of the ACM, 45(6): 983-1006, 1998.
206. M. Kearns, and L. G. Valiant. Cryptographic limitations on learning Boolean formulae, and finite automata. In Proceedings of the 21st Annual ACM Symposiumon Theory of Computing, pages 433-444, Seattle, WA, 1989.
207. M. J. Kearns, and U. V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, Cambridge, MA, 1994.
208. J. Kittler, and F.M. Alkoot. Sum versus vote fusion inmultiple classifier systems. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 25(1): 110-115, 2003.
209. J. Kittler, M. Hatef, R. Duin, and J. Matas. On combining classifiers. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 20(3): 226-239, 1998.
210. E.M. Kleinberg. On the algorithmic implementation of stochastic discrimination. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 22(5): 473-490, 2000.
211. D. E. Knuth. The Art of Computer Programming, Volume 3: Sorting, and Searching. Addison-Wesley, Reading, MA, 2nd edition, 1997.
212. A. H. Ko, R. Sabourin, and J. A. S. Britto. From dynamic classifier selection to dynamic ensemble selection. Pattern Recognition, 41(5): 1718-1731, 2008.
213. R. Kohavi, and D. H. Wolpert. Bias plus variance decomposition for zeroone loss functions. In Proceedings of the 13th International Conference onMachine Learning, pages 275-283, Bari, Italy, 1996.
214. T. Kohonen. Self-Organization, and Associative Memory. Springer-Verlag, Berlin, 3rd edition, 1989.
215. J. F. Kolen, and J. B. Pollack. Back propagation is sensitive to initial conditions. In R. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances inNeural Information Processing Systems 3, pages 860-867. Morgan Kaufmann, San Francisco, CA, 1991.
216. J. Z. Kolter, and M. A. Maloof. Learning to detect, and classify malicious executables in the wild. Journal ofMachine Learning Research, 7: 2721-2744, 2006.
217. E. B. Kong, and T. G. Dietterich. Error-correcting output coding corrects bias, and variance. In Proceedings of the 12th International Conference on Machine Learning, pages 313-321, Tahoe City, CA, 1995.
218. A. Krogh, and J. Vedelsby. Neural network ensembles, cross validation, and active learning. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances inNeural Information Processing Systems 7, pages 231-238. MIT Press, Cambridge, MA, 1995.
219. M. Kubat, and S. Matwin. Addressing the curse of imbalanced training sets: One sided selection. In Proceedings of the 14th Intenational Conference onMachine Learning, pages 179-186, Nashville, TN, 1997.
220. H. W. Kuhn. The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2: 83-79, 1955.
221. M. Kukar, and I. Kononenko. Cost-sensitive learning with neural networks. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 445-449, Brighton, UK, 1998.
222. L. I. Kuncheva. A theoretical study on six classifier fusion strategies. IEEE Transations on Pattern Analysis, and Machine Intelligence, 24(2): 281-286, 2002.
223. L. I. Kuncheva. Combining Pattern Classifiers: Methods, and Algorithms. JohnWiley & Sons, Hoboken, NJ, 2004.
224. L. I. Kuncheva. Classifier ensembles: Facts, fiction, faults, and future, 2008. Plenary Talk at the 19th International Conference on Pattern Recognition.
225. L. I. Kuncheva, and S. T. Hadjitodorov. Using diversity in cluster ensembles. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pages 1214-1219, Hague, The Netherlands, 2004.
226. L. I. Kuncheva, and D. P. Vetrov. Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 28(11): 1798-1808, 2006.
227. L. I. Kuncheva, and C. J. Whitaker. Measures of diversity in classifier ensembles, and their relationship with the ensemble accuracy. Machine Learning, 51(2): 181-207, 2003.
228. L. I. Kuncheva, J. C. Bezdek, and R. P. Duin. Decision templates formultiple classifier fusion: An experimental comparison. Pattern Recognition, 34(2): 299-314, 2001.
229. L. I. Kuncheva, C. J. Whitaker, C. Shipp, and R. Duin. Limits on themajority vote accuracy in classifier fusion. Pattern Analysis, and Applications, 6(1): 22-31, 2003.
230. L. I. Kuncheva, S. T. Hadjitodorov, and L. P. Todorova. Experimental comparison of cluster ensemblemethods. In Proceedings of the 9th International Conference on Information Fusion, pages 1-7, Florence, Italy, 2006.
231. S. Kutin, and P. Niyogi. Almost-everywhere algorithmic stability, and generalization error. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, pages 275-282, Edmonton, Canada, 2002.
232. S. W. Kwok, and C. Carter. Multiple decision trees. In Proceedings of the 4th International Conference on Uncertainty in Artificial Intelligence, pages 327-338, New York, NY, 1988.
233. L. Lam, and S. Y. Suen. Application ofmajority voting to pattern recognition: An analysis of its behavior, and performance. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems, and Humans, 27(5): 553-568, 1997.
234. A. Lazarevic, and Z. Obradovic. Effective pruning of neural network classifier ensembles. In Proceedings of the IEEE/INNS International Joint Conference on Neural Networks, pages 796-801, Washington, DC, 2001.
235. D. Lewis, and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research, and Development in Information Retrieval, pages 3-12, Dublin, Ireland, 1994.
236. M. Li, and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems, and Humans, 37(6): 1088-1098, 2007.
237. M. Li, W. Wang, and Z.-H. Zhou. Exploiting remote learners in internet environment with agents. Science China: Information Sciences, 53(1): 64-76, 2010.
238. N. Li, and Z.-H. Zhou. Selective ensemble under regularization framework. In Proceedings of the 8th International Workshop Multiple Classifier Systems, pages 293-303, Reykjavik, Iceland, 2009.
239. S. Z. Li, Q. Fu, L. Gu, B. Schölkopf, and H. J. Zhang. Kernel machine based learning for multi-view face detection, and pose estimation. In Proceedings of the 8th International Conference on Computer Vision, pages 674-679, Vancouver, Canada, 2001.
240. R. Liere, and P. Tadepalli. Active learning with committees for text categorization. In Proceedings of the 14th National Conference on Artificial Intelligence, pages 591-596, Providence, RI, 1997.
241. H.-T. Lin, and L. Li. Support vector machinery for infinite ensemble learning. Journal ofMachine Learning Research, 9: 285-312, 2008.
242. X. Lin, S. Yacoub, J. Burns, and S. Simske. Performance analysis of pattern classifier combination by plurality voting. Pattern Recognition Letters, 24(12): 1959-1969, 2003.
243. Y. M. Lin, Y. Lee, and G. Wahba. Support vectormachines for classification in nonstandard situations. Machine Learning, 46(1): 191-202, 2002.
244. F. T. Liu, K. M. Ting, and W. Fan. Maximizing tree diversity by building complete-random decision trees. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery, and Data Mining, pages 605-610, Hanoi, Vietnam, 2005.
245. F. T. Liu, K. M. Ting, Y. Yu, and Z.-H. Zhou. Spectrum of variable-random trees. Journal of Artificial Intelligence Research, 32(1): 355-384, 2008a.
246. F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining, pages 413-422, Pisa, Italy, 2008b.
247. F. T. Liu, K.M. Ting, and Z.-H. Zhou. On detecting clustered anomalies using SCiForest. In Proceedings of the European Conference on Machine Learning, and Principles, and Practice of Knowledge Discovery in Databases, pages 274-290, Barcelona, Spain, 2010.
248. X.-Y. Liu, and Z.-H. Zhou. The influence of class imbalance on cost-sensitive learning: An empirical study. In Proceedings of the 6th IEEE International Conference on Data Mining, pages 970-974, Hong Kong, China, 2006.
249. X.-Y. Liu, and Z.-H. Zhou. Learning with cost intervals. In Proceedings of the 16th ACMSIGKDD International Conference on Knowledge Discovery, and Data Mining, pages 403-412, Washington, DC, 2010.
250. X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for classimbalance learning. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 39(2): 539-550, 2009.
251. Y. Liu, and X. Yao. Ensemble learning via negative correlation. Neural Networks, 12(10): 1399-1404, 1999.
252. S. P. Lloyd. Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2): 128-137, 1982.
253. J. M. Lobo, A. Jiménez-Valverde, and R. Real. AUC: Amisleadingmeasure of the performance of predictive distribution models. Global Ecology, and Biogeography, 17(2): 145-151, 2008.
254. B. Long, Z. Zhang, and P. S. Yu. Combiningmultiple clusterings by soft correspondence. In Proceedings of the 4th IEEE International Conference on Data Mining, pages 282-289, Brighton, UK, 2005.
255. P. K. Mallapragada, R. Jin, A. K. Jain, and Y. Liu. Semiboost: Boosting for semi-supervised learning. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 30(11): 2000-2014, 2009.
256. I. Maqsood, M. R. Khan, and A. Abraham. An ensemble of neural networks for weather forecasting. Neural Computing & Applications, 13(2): 112-122, 2004.
257. D. D. Margineantu, and T. G. Dietterich. Pruning adaptive boosting. In Proceedings of the 14th International Conference onMachine Learning, pages 211-218, Nashville, TN, 1997.
258. H. Markowitz. Portfolio selection. Journal of Finance, 7(1): 77-91, 1952.
259. G. Martínez-Muñoz, and A. Suárez. Aggregation ordering in bagging. In Proceedings of the IASTED International Conference on Artifical Intelligence, and Applications, pages 258-263, Innsbruck, Austria, 2004.
260. G. Martínez-Muñoz, and A. Suárez. Pruning in ordered bagging ensembles. In Proceedings of the 23rd International Conference onMachine Learning, pages 609-616, Pittsburgh, PA, 2006.
261. G. Martínez-Muñoz, and A. Suárez. Using boosting to prune bagging ensembles. Pattern Recognition Letters, 28(1): 156-165, 2007.
262. G. Martínez-Muñoz, D. Hernández-Lobato, and A. Suárez. An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Transaction on Pattern Analysis, and Machine Intelligence, 31(2): 245-259, 2009.
263. H. Masnadi-Shirazi, and N. Vasconcelos. Asymmetric Boosting. In Proceedings of the 24th International Conference onMachine Learning, pages 609-616, Corvallis, OR, 2007.
264. L. Mason, J. Baxter, P. L. Bartlett, and M. Frean. Functional gradient techniques for combining hypotheses. In P. J. Bartlett, B. Schölkopf, D. Schuurmans, and A. J. Smola, editors, Advances in Large-Margin Classifiers, pages 221-246. MIT Press, Cambridge, MA, 2000.
265. A. Maurer, and M. Pontil. Empirical Bernstein bounds, and sample-variance penalization. In Proceedings of the 22nd Conference on Learning Theory, Montreal, Canada, 2009.
266. A. McCallum, and K. Nigam. Employing EM and pool-based active learning for text classification. In Proceedings of the 15th International Conference onMachine Learning, pages 350-358, Madison, WI, 1998.
267. R. A. McDonald, D. J. Hand, and I. A. Eckley. An empirical comparison of three boosting algorithms on real data sets with artificial class noise. In Proceedings of the 4th International Workshop on Multiple Classifier Systems, pages 35-44, Guilford, UK, 2003.
268. W. McGill. Multivariate information transmission. IEEE Transactions on Information Theory, 4(4): 93-111, 1954.
269. D. Mease, and A. Wyner. Evidence contrary to the statistical view of boosting (with discussions). Journal ofMachine Learning Research, 9: 131-201, 2008.
270. N. Meinshausen, and P. Bühlmann. Stability selection. Journal of the Royal Statistical Society: Series B, 72(4): 417-473, 2010.
271. P. Melville, and R. J. Mooney. Creating diversity in ensembles using artificial data. Information Fusion, 6(1): 99-111, 2005.
272. C. E. Metz. Basic principles of ROC analysis. Seminars in NuclearMedicine, 8(4): 283-298, 1978.
273. D. J. Miller, and H. S. Uyar. A mixture of experts classifier with learning based on both labelled, and unlabelled data. In M. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 571-577. MIT Press, Cambridge, MA, 1997.
274. T. M. Mitchell. Machine Learning. McGraw-Hill, New York, NY, 1997.
275. X. Mu, P. Watta, and M. H. Hassoun. Analysis of a plurality voting-based combination of classifiers. Neural Processing Letters, 29(2): 89-107, 2009.
276. I. Mukherjee, and R. Schapire. A theory of multiclass boosting. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems 23, pages 1714-1722. MIT Press, Cambridge, MA, 2010.
277. S. K. Murthy, S. Kasif, and S. Salzberg. A systemfor the induction of oblique decision trees. Journal of Artificial Intelligence Research, 2: 1-33, 1994.
278. A.M. Narasimhamurthy. A framework for the analysis of majority voting. In Proceedings of the 13th Scandinavian Conference on Image Analysis, pages 268-274, Halmstad, Sweden, 2003.
279. A. Narasimhamurthy. Theoretical bounds of majority voting performance for a binary classification problem. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 27(12): 1988-1995, 2005.
280. S. Nash, and A. Sofer. Linear, and Nonlinear Programming. McGraw-Hill, New York, NY, 1996.
281. R. Ng, and J. Han. Efficient, and effective clustering method for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases, pages 144-155, Santiago, Chile, 1994.
282. H. T. Nguyen, and A. W. M. Smeulders. Active learning using pre-clustering. In Proceedings of the 21st International Conference onMachine Learning, pages 623-630, Banff, Canada, 2004.
283. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled, and unlabeled documents using EM. Machine Learning, 39(2-3): 103-134, 2000.
284. N. J. Nilsson. Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill, New York, NY, 1965.
285. D. Opitz, and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11: 169-198, 1999.
286. S. Panigrahi, A. Kundu, S. Sural, and A. K. Majumdar. Credit card fraud detection: A fusion approach using Dempster-Shafer theory, and Bayesian learning. Information Fusion, 10(4): 354-363, 2009.
287. I. Partalas, G. Tsoumakas, and I. Vlahavas. Pruning an ensemble of classifiers via reinforcement learning. Neurocomputing, 72(7-9): 1900-1909, 2009.
288. D. Partridge, and W. J. Krzanowski. Software diversity: Practical statistics for its measurement, and exploitation. Information & Software Technology, 39(10): 707-717, 1997.
289. A. Passerini, M. Pontil, and P. Frasconi. New results on error correcting output codes of kernel machines. IEEE Transactions on Neural Networks, 15(1): 45-54, 2004.
290. M. P. Perrone, and L. N. Cooper. When networks disagree: Ensemble method for neural networks. In R. J. Mammone, editor, Artificial Neural Networks for Spech, and Vision, pages 126-142. Chapman & Hall, New York, NY, 1993.
291. J. C. Platt. Probabilities for SVmachines. In Advances in LargeMargin Classifiers, pages 61-74. MIT Press, Cambridge, MA, 2000.
292. R. Polikar, A. Topalis, D. Parikh, D. Green, J. Frymiare, J. Kounios, and C.M. Clark. An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease. Information Fusion, 9(1): 83-95, 2008.
293. B. R. Preiss. Data Structures, and Algorithms with Object-Oriented Design Patterns in Java. Wiley, Hoboken, NJ, 1999.
294. O. Pujol, P. Radeva, and J. Vitrià. Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 28(6): 1007-1012, 2006.
295. O. Pujol, S. Escalera, and P. Radeva. An incremental node embedding technique for error correcting output codes. Pattern Recognition, 41(2): 713-725, 2008.
296. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA, 1993.
297. J. R. Quinlan. Induction of decision trees. Machine Learning, 1(1): 81-106, 1998.
298. Š. Raudys, and F. Roli. The behavior knowledge space fusion method: Analysis of generalization error, and strategies for performance improvement. In Proceedings of the 4th International Workshop on Multiple Classifier Systems, pages 55-64, Guildford, UK, 2003.
299. R. A. Redner, and H. F. Walker. Mixture densities, maximum likelihood, and the EMalgorithm. SIAMReview, 26(2): 195-239, 1984.
300. L. Reyzin, and R. E. Schapire. How boosting the margin can also boost classifier complexity. In Proceedings of the 23rd International Conference on Machine Learning, pages 753-760, Pittsburgh, PA, 2006.
301. B. Ripley. Pattern Recognition, and Neural Networks. Cambridge University Press, Cambridge, UK, 1996.
302. M. Robnik-Šikonja. Improving random forests. In Proceedings of the 15th European Conference on Machine Learning, pages 359-370, Pisa, Italy, 2004.
303. J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso. Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 28(10): 1619-1630, 2006.
304. G. Rogova. Combining the results of several neural network classifiers. Neural Networks, 7(5): 777-781, 1994.
305. L. Rokach. Pattern ClassificationUsing EnsembleMethods. World Scientific, Singapore, 2010.
306. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart, and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1, pages 318-362. MIT Press, Cambridge, MA, 1986.
307. D. Ruta, and B. Gabrys. Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting. In Proceedings of the 2nd InternationalWorkshop onMultiple Classifier Systems, pages 399-408, Cambridge, UK, 2001.
308. R. E. Schapire. The strength of weak learnability. Machine Learning, 5(2): 197-227, 1990.
309. R. E. Schapire, and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3): 297-336, 1999.
310. R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee. Boosting the margin: A new explanation for the effectiveness of votingmethods. Annals of Statistics, 26(5): 1651-1686, 1998.
311. J. Schiffers. A classification approach incorporating misclassification costs. Intelligent Data Analysis, 1(1): 59-68, 1997.
312. B. Schölkopf, and A. J. Smola. Learning with Kernels. MITPress, Cambridge, MA, 2002.
313. B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors. Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, MA, 1999.
314. M. G. Schultz, E. Eskin, E. Zadok, and S. J. Stolfo. Data mining methods for detection of new malicious executables. In Proceedings of the IEEE Symposiumon Security, and Privacy, pages 38-49, Oakland, CA, 2001.
315. A. K. Seewald. How to make stacking better, and faster while also taking care of an unknown weakness. In Proceedings of the 19th International Conference onMachine Learning, pages 554-561, Sydney, Australia, 2002.
316. B. Settles. Active learning literature survey. Technical Report 1648, Department of Computer Sciences, University of Wisconsin at Madison, Madison, WI, 2009.
317. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the 5th Annual ACM Conference on Computational Learning Theory, pages 287-294, Pittsburgh, PA, 1992.
318. G. Shafer. A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ, 1976.
319. G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multiresolution clustering approach for very large spatial databases. In Proceedings of the 24th International Conference on Very Large Data Bases, pages 428-439, New York, NY, 1998.
320. H. B. Shen, and K. C. Chou. Ensemble classifier for protein fold pattern recognition. Bioinformatics, 22(14): 1717-1722, 2006.
321. J. Shi, and J. Malik. Normalized cuts, and image segmentation. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 22(8): 888-905, 2000.
322. C. A. Shipp, and L. I. Kuncheva. Relationships between combination methods, and measures of diversity in combining classifiers. Information Fusion, 3(2): 135-148, 2002.
323. D. B. Skalak. The sources of increased accuracy for two proposed boosting algorithms. InWorking Notes of the AAAI’96Workshop on Integrating Multiple LearnedModels, Portland, OR, 1996.
324. N. Slonim, N. Friedman, and N. Tishby. Multivariate information bottleneck. Neural Computation, 18(8): 1739-1789, 2006.
325. P. Smyth, and D. Wolpert. Stacked density estimation. In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems 10, pages 668-674. MIT Press, Cambridge, MA, 1998.
326. P. H. A. Sneath, and R. R. Sokal. Numerical Taxonomy: The Principles, and Practice of Numerical Classification. W. H. Freeman, San Francisco, CA, 1973.
327. V. Soto, G. Martínez-Muñoz, D. Hernández-Lobato, and A. Suárez. A double pruning algorithm for classification ensembles. In Proceedings of 9th International Workshop Multiple Classifier Systems, pages 104-113, Cairo, Egypt, 2010.
328. K. A. Spackman. Signal detection theory: Valuable tools for evaluating inductive learning. In Proceedings of the 6th International Workshop on Machine Learning, pages 160-163, Ithaca, NY, 1989.
329. A. Strehl, and J. Ghosh. Cluster ensembles - A knowledge reuse framework for combiningmultiple partitions. Journal ofMachine Learning Research, 3: 583-617, 2002.
330. A. Strehl, J. Ghosh, and R. J. Mooney. Impact of similaritymeasures onwebpage clustering. In Proceedings of the AAAI’2000Workshop on AI for Web Search, pages 58-64, Austin, TX, 2000.
331. M. Studeny, and J. Vejnarova. The multi-information function as a tool for measuring stochastic dependence. In M. I. Jordan, editor, Learning in Graphical Models, pages 261-298. Kluwer, Norwell, MA, 1998.
332. Y. Sun, A. K. C. Wong, and Y. Wang. Parameter inference of cost-sensitive boosting algorithms. In Proceedings of the 4th International Conference on Machine Learning, and Data Mining in Pattern Recognition, pages 21-30, Leipzig, Germany, 2005.
333. C. Tamon, and J. Xiang. On the boosting pruning problem. In Proceedings of the 11th European Conference on Machine Learning, pages 404-412, Barcelona, Spain, 2000.
334. A. C. Tan, D. Gilbert, and Y. Deville. Multi-class protein fold classification using a new ensemblemachine learning approach. Genome Informatics, 14: 206-217, 2003.
335. P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison-Wesley, Upper Saddle River, NJ, 2006.
336. E. K. Tang, P. N. Suganthan, and X. Yao. An analysis of diversity measures. Machine Learning, 65(1): 247-271, 2006.
337. J. W. Taylor, and R. Buizza. Neural network load forecasting with weather ensemble predictions. IEEE Transactions on Power Systems, 17(3): 626-632, 2002.
338. S. Theodoridis, and K. Koutroumbas. Pattern Recognition. AcademicPress, New York, NY, 4th edition, 2009.
339. R. Tibshirani. Regression shrinkage, and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1): 267-288, 1996a.
340. R. Tibshirani. Bias, variance, and prediction error for classification rules. Technical report, Department of Statistics, University of Toronto, 1996b.
341. A. B. Tickle, R. Andrews, M. Golea, and J. Diederich. The truth will come to light: Directions, and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks, 9(6): 1057-1067, 1998.
342. K. M. Ting. A comparative study of cost-sensitive boosting algorithms. In Proceedings of the 17th International Conference on Machine Learning, pages 983-990, San Francisco, CA, 2000.
343. K. M. Ting. An instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge, and Data Engineering, 14(3): 659-665, 2002.
344. K. M. Ting, and I. H. Witten. Issues in stacked generalization. Journal of Artificial Intelligence Research, 10: 271-289, 1999.
345. I. Tomek. Two modifications of CNN. IEEE Transactions on Systems, Man, and Cybernetics, 6(11): 769-772, 1976.
346. S. Tong, and D. Koller. Support vectormachine active learning with applications to text classification. In Proceedings of the 17th International Conference onMachine Learning, pages 999-1006, San Francisco, CA, 2000.
347. A. Topchy, A. K. Jain, and W. Punch. Combining multiple weak clusterings. In Proceedings of the 3rd IEEE International Conference on Data Mining, pages 331-338, Melbourne, FL, 2003.
348. A. Topchy, A. K. Jain, and W. Punch. Amixturemodel for clustering ensembles. In Proceedings of the 4th SIAM International Conference on Data Mining, pages 379-390, Lake Buena Vista, FL, 2004a.
349. A. Topchy, B. Minaei-Bidgoli, A. K. Jain, and W. F. Punch. Adaptive clustering ensembles. In Proceedings of the 17th InternationalConference on Pattern Recognition, pages 272-275, Cambridge, UK, 2004b.
350. A. P. Topchy, M. H. C. Law, A. K. Jain, and A. L. Fred. Analysis of consensus partition in cluster ensemble. In Proceedings of the 4th IEEE International Conference on Data Mining, pages 225-232, Brighton, UK, 2004c.
351. G. Tsoumakas, I. Katakis, and I. Vlahavas. Effective voting of heterogeneous classifiers. In Proceedings of the 15th European Conference on Machine Learning, pages 465-476, Pisa, Italy, 2004.
352. G. Tsoumakas, L. Angelis, and I. P. Vlahavas. Selective fusion of heterogeneous classifiers. Intelligent Data Analysis, 9(6): 511-525, 2005.
353. G. Tsoumakas, I. Partalas, and I. Vlahavas. An ensemble pruning primer. In O. Okun, and G. Valentini, editors, Applications of Supervised, and Unsupervised Ensemble Methods, pages 155-165. Springer, Berlin, 2009.
354. K. Tumer. Linear, and Order Statistics Combiners for Reliable Pattern Classification. PhD thesis, The University of Texas at Austin, 1996.
355. K. Tumer, and J. Ghosh. Theoretical foundations of linear, and order statistics combiners for neural pattern classifiers. Technical Report TR-95-02-98, Computer, and Vision Research Center, University of Texas, Austin, 1995.
356. K. Tumer, and J. Ghosh. Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition, 29(2): 341-348, 1996.
357. P. D. Turney. Types of cost in inductive concept learning. In Proceedings of the ICML’2000 Workshop on Cost-Sensitive Learning, pages 15-21, San Francisco, CA, 2000.
358. N. Ueda, and R. Nakano. Generalization error of ensemble estimators. In Proceedings of the IEEE International Conference on Neural Networks, pages 90-95, Washington, DC, 1996.
359. W. Utschick, and W. Weichselberger. Stochastic organization of output codes in multiclass learning problems. Neural Computation, 13(5): 1065-1102, 2004.
360. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11): 1134-1142, 1984.
361. H. Valizadegan, R. Jin, and A. K. Jain. Semi-supervised boosting for multiclass classification. In Proceedings of the 19th European Conference on Machine Learning, pages 522-537, Antwerp, Belgium, 2008.
362. C. van Rijsbergen. Information Retrieval. Butterworths, London, 1979.
363. V. N. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.
364. P. Viola, and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Computer Society Conference on Computer Vision, and Pattern Recognition, pages 511-518, Kauai, HI, 2001.
365. P. Viola, and M. Jones. Fast, and robust classification using asymmetric Adaboost, and a detector cascade. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 1311-1318. MIT Press, Cambridge, MA, 2002.
366. P. Viola, and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2): 137-154, 2004.
367. L. Wang, M. Sugiyama, C. Yang, Z.-H. Zhou, and J. Feng. On the margin explanation of boosting algorithm. In Proceedings of the 21st Annual Conference on Learning Theory, pages 479-490, Helsinki, Finland, 2008.
368. W. Wang, and Z.-H. Zhou. On multi-view active learning, and the combination with semi-supervised learning. In Proceedings of the 25th International Conference on Machine Learning, pages 1152-1159, Helsinki, Finland, 2008.
369. W. Wang, and Z.-H. Zhou. Multi-view active learning in the non-realizable case. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta, editors, Advances inNeural Information Processing Systems 23, pages 2388-2396. MIT Press, Cambridge, MA, 2010.
370. W. Wang, J. Yang, and R. Muntz. STING: A statistical information grid approach to spatial data mining. In Proceedings of the 23rd International Conference on Very Large Data Bases, pages 186-195, Athens, Greece, 1997.
371. M. K. Warmuth, K. A. Glocer, and S. V. Vishwanathan. Entropy regularized LPBoost. In Proceedings of the 19th International Conference on Algorithmic Learning Theory, pages 256-271, Budapest, Hungary, 2008.
372. S. Watanabe. Information theoretical analysis of multivariate correlation. IBMJournal of Research, and Development, 4(1): 66-82, 1960.
373. S. Waterhouse, D. Mackay, and T. Robinson. Bayesian methods for mixtures of experts. In D. S. Touretzky, M. Mozer, and M. E. Hasselmo, editors, Advances inNeural Information Processing Systems 8, pages 351-357. MIT Press, Cambridge, MA, 1996.
374. S. R. Waterhouse, and A. J. Robinson. Constructive algorithms for hierarchical mixtures of experts. In D. S. Touretzky, M. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 8, pages 584-590. MIT Press, Cambridge, MA, 1996.
375. C. Watkins, and P. Dayan. Q-learning. Machine Learning, 8(3): 279-292, 1992.
376. G. I. Webb, and Z. Zheng. Multistrategy ensemble learning: Reducing error by combining ensemble learning techniques. IEEE Transactions on Knowledge, and Data Engineering, 16(8): 980-991, 2004.
377. G. I. Webb, J. R. Boughton, and Z. Wang. Not so naïve Bayes: Aggregating one-dependence estimators. Machine Learning, 58(1): 5-24, 2005.
378. P. Werbos. Beyond regression: New tools for prediction, and analysis in the behavior science. PhD thesis, Harvard University, Cambridge, MA, 1974.
379. D. West, S. Dellana, and J. Qian. Neural network ensemble strategies for financial decision applications. Computers & Operations Research, 32(10): 2543-2559, 2005.
380. T. Windeatt, and R. Ghaderi. Coding, and decoding strategies formulti-class learning problems. Information Fusion, 4(1): 11-21, 2003.
381. D.H. Wolpert. Stacked generalization. Neural Networks, 5(2): 241-260, 1992.
382. D.H. Wolpert. The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7): 1341-1390, 1996.
383. D.H. Wolpert, and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1): 67-82, 1997.
384. D. H. Wolpert, and W. G. Macready. An efficient method to estimate bagging’s generalization error. Machine Learning, 35(1): 41-55, 1999.
385. K. Woods, W. P. Kegelmeyer, and K. Bowyer. Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 19(4): 405-410, 1997.
386. J. Wu, S. C. Brubaker, M.D. Mullin, and J.M. Rehg. Fast asymmetriclearning for cascade face detection. IEEE Transactions on Pattern Analysis, and Machine Intelligence, 30(3): 369-382, 2008.
387. L. Xu, and S. Amari. Combining classifiers, and learning mixture-of-experts. In J. R. R. Dopico, J. Dorado, and A. Pazos, editors, Encyclopedia of Artificial Intelligence, pages 318-326. IGI, Berlin, 2009.
388. L. Xu, and M. I. Jordan. On convergence properties of the EM algorithm for Gaussian mixtures. Neural Computation, 8(1): 129-151, 1996.
389. L. Xu, A. Krzyzak, and C. Y. Suen. Methods of combiningmultiple classifiers, and their applications to handwriting recognition. IEEE Transactions on SystemsMan, and Cybernetics, 22(3): 418-435, 1992.
390. L. Xu, M. I. Jordan, and G. E. Hinton. An alternative model for mixtures of experts. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 633-640. MIT Press, Cambridge, MA, 1995.
391. L. Yan, R. H. Dodier, M. Mozer, and R. H. Wolniewicz. Optimizing classifier performance via an approximation to the Wilcoxon-Mann-Whitney statistic. In Proceedings of the 20th International Conference on Machine Learning, pages 848-855, Washington, DC, 2003.
392. W. Yan, and F. Xue. Jet engine gas path fault diagnosis using dynamic fusion ofmultiple classifiers. In Proceedings of the International Joint Conference on Neural Networks, pages 1585-1591, Hong Kong, China, 2008.
393. Y. Yu, Y.-F. Li, and Z.-H. Zhou. Diversity regularizedmachine. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pages 1603-1608, Barcelona, Spain, 2011.
394. Z. Yu, and H.-S. Wong. Class discovery from gene expression data based on perturbation, and cluster ensemble. IEEE Transactions on NanoBioscience, 18(2): 147-160, 2009.
395. G. Yule. On the association of attributes in statistics. Philosophical Transactions of the Royal Society of London, 194: 257-319, 1900.
396. B. Zadrozny, and C. Elkan. Learning, and making decisions when costs, and probabilities are both unknown. In Proceedings of the 7th ACM SIGKDDInternational Conference on Knowledge Discovery, and DataMining, pages 204-213, San Francisco, CA, 2001a.
397. B. Zadrozny, and C. Elkan. Obtaining calibrated probability estimates from decision trees, and naive Bayesian classifiers. In Proceedings of the 18th International Conference on Machine Learning, pages 609-616, Williamstown, MA, 2001b.
398. B. Zadrozny, J. Langford, and N. Abe. Cost-sensitive learning by costproportionate example weighting. In Proceedings of the 3rd IEEE International Conference on Data Mining, pages 435-442, Melbourne, FL, 2003.
399. M.-L. Zhang, and Z.-H. Zhou. Exploiting unlabeled data to enhance ensemble diversity. In Proceedings of the 9th IEEE International Conference on Data Mining, pages 609-618, Sydney, Australia, 2010.
400. T. Zhang. Analysis of regularized linear functions for classification problems. Technical Report RC-21572, IBM, 1999.
401. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Canada, 1996.
402. X. Zhang, S. Wang, T. Shan, and L. Jiao. Selective SVMs ensemble driven by immune clonal algorithm. In Proceedings of the EvoWorkshops, pages 325-333, Lausanne, Switzerland, 2005.
403. X. Zhang, L. Jiao, F. Liu, L. Bo, and M. Gong. Spectral clustering ensemble applied to SAR image segmentation. IEEE Transactions onGeoscience, and Remote Sensing, 46(7): 2126-2136, 2008.
404. Y. Zhang, S. Burer, and W. N. Street. Ensemble pruning via semi-definite programming. Journal ofMachine Learning Research, 7: 1315-1338, 2006.
405. Z. Zheng, and G. I. Webb. Laze learning of Bayesian rules. Machine Learning, 41(1): 53-84, 2000.
406. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. Learning with local, and global consistency. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.
407. Z.-H. Zhou. Rule extraction: Using neural networks or for neural networks? Journal of Computer Science, and Technology, 19(2): 249-253, 2004.
408. Z.-H. Zhou. Comprehensibility of data mining algorithms. In J. Wang, editor, Encyclopedia of Data Warehousing, and Mining, pages 190-195. IGI, Hershey, PA, 2005.
409. Z.-H. Zhou. When semi-supervised learning meets ensemble learning. In Proceedings of the 8th International Workshop on Multiple Classifier Systems, pages 529-538, Reykjavik, Iceland, 2009.
410. Z.-H. Zhou. When semi-supervised learning meets ensemble learning. Frontiers of Electrical, and Electronic Engineering in China, 6(1): 6-16, 2011.
411. Z.-H. Zhou, and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 7(1): 37-42, 2003.
412. Z.-H. Zhou, and Y. Jiang. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge, and Data Engineering, 16(6): 770-773, 2004.
413. Z.-H. Zhou, and M. Li. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge, and Data Engineering, 17(11): 1529-1541, 2005.
414. Z.-H. Zhou, and M. Li. Semi-supervised regression with co-training style algorithms. IEEE Transactions on Knowledge, and Data Engineering, 19(11): 1479-1493, 2007.
415. Z.-H. Zhou, and M. Li. Semi-supervised learning by disagreement. Knowledge, and Information Systems, 24(3): 415-439, 2010a.
416. Z.-H. Zhou, and N. Li. Multi-information ensemble diversity. In Proceedings of the 9th International Workshop on Multiple Classifier Systems, pages 134-144, Cairo, Egypt, 2010b.
417. Z.-H. Zhou, and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge, and Data Engineering, 18(1): 63-77, 2006.
418. Z.-H. Zhou, and X.-Y. Liu. On multi-class cost-sensitive learning. Computational Intelligence, 26(3): 232-257, 2010.
419. Z.-H. Zhou, and W. Tang. Selective ensemble of decision trees. In Proceedings of the 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pages 476-483, Chongqing, China, 2003.
420. Z.-H. Zhou, and W. Tang. Clusterer ensemble. Knowledge-Based Systems, 19(1): 77-83, 2006.
421. Z.-H. Zhou, and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 35(4): 725-735, 2005.
422. Z.-H. Zhou, Y. Jiang, Y.-B. Yang, and S.-F. Chen. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine, 24(1): 25-36, 2002a.
423. Z.-H. Zhou, J. Wu, and W. Tang. Ensembling neural networks: Many could be better than all. Artificial Intelligence, 137(1-2): 239-263, 2002b.
424. Z.-H. Zhou, Y. Jiang, and S.-F. Chen. Extracting symbolic rules fromtrained neural network ensembles. AI Communications, 16(1): 3-15, 2003.
425. Z.-H. Zhou, K.-J. Chen, and H.-B. Dai. Enhancing relevance feedback in image retrieval using unlabeled data. ACM Transactions on Information Systems, 24(2): 219-244, 2006.
426. J. Zhu, S. Rosset, H. Zou, and T. Hastie. Multi-class AdaBoost. Technical report, Department of Statistics, University of Michigan, Ann Arbor, MI, 2006.
427. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Department of Computer Sciences, University of Wisconsin at Madison, Madison, WI, 2006. http://www.cs.wisc.edu/jerryzhu/pub/sslsurvey.pdf.
428. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using Gaussian fields, and harmonic functions. In Proceedings of the 20th International Conference on Machine Learning, pages 912-919, Washington, DC, 2003.
429. X. Zhu, X. Wu, and Y. Yang. Dynamic classifier selection for effectivemining from noisy data streams. In Proceedings of the 14th IEEE International Conference on Data Mining, pages 305-312, Brighton, UK, 2004.