Induction of multiclass multifeature split decision trees from distributed data

Pattern Recognition

Volumn 42, Issue 9, 2009, Pages 1786-1794

  Ouyang, Jie ^a   Patel, Nilesh ^a   Sethi, Ishwar ^a  

^a Oakland University   (United States)

Author keywords

Decision tree; Distributed data mining; Fisher linear discriminant

Indexed keywords

COMPACT TREE; DATA SETS; DECISION TREE INDUCTION; DISTRIBUTED DATA; DISTRIBUTED DATA MINING; DISTRIBUTED DATABASE; EXISTING METHOD; FISHER LINEAR DISCRIMINANT; FISHER'S LINEAR DISCRIMINANT; GEOGRAPHICALLY DISPERSED LOCATIONS; MULTI-CLASS; MULTIPLE CLASS; TRAINING DATA;

DISCRIMINANT ANALYSIS; INFORMATION MANAGEMENT; LOCATION; MINING; PATTERN RECOGNITION;

DECISION TREES;

EID: 67349275042     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.01.033     Document Type: Article

References (41)

1. A. Asuncion, D. Newman, UCI machine learning repository, 2007 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
2. Baik S., and Bala J.W. A decision tree algorithm for distributed data mining: towards network intrusion detection. ICCSA (2004)
3. Bar-Or A., Wolff A.S.R., and Keren D. Decision tree induction in high dimensional, hierarchically distributed databases. Proceedings of 2005 SIAM International Conference on Data Mining SDM'05, Newport Beach, CA (2005)
4. Breiman L. Random forests. Machine Learning 45 1 (2001) 5-32
5. Breiman L., Friedman J., Olshen R., and Stone C. Classification and Regression Trees (1984), Wadsworth and Brooks, Monterey, CA
6. Caragea D., Silvescu A., and Honavar V. Decision tree induction from distributed heterogeneous autonomous data sources. Proceedings of the 3rd International Conference on Intelligent Systems Design and Applications, Tulsa, OK (2003)
7. Caragea D., Silvescu A., and Honavar V. A framework for learning from distributed data using sufficient statistics and its application to learning decision trees. International Journal of Hybrid Intelligent Systems 1 2 (2004) 80-89
8. Castillo J.A.R., Silvescu A., Caragea D., Pathak J., and Honavar V.G. Information extraction and integration from heterogeneous, distributed, autonomous information sources ca federated ontology-driven, query-centric approach. IEEE International Conference on Information Integration and Reuse, Las Vegas, Nevada (2003)
9. Chawla N.V., Moore T.E., Hall L.O., Bowyer K.W., Kegelmeyer W.P., and Springer C. Distributed learning with bagging-like performance. Pattern Recognition Letters 24 1-3 (2003) 455-471
10. Chen L.-F., Liao H.-Y.M., Ko M.-T., Lin J.-C., and Yu G.-J. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition 33 (2000) 1713-1726
11. Draper B., Brodley C., and Utgoff P. Goal-directed classification using linear machine decision trees. Pattern Analysis and Machine Intelligence 16 9 (1994) 888-893
12. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification (2000), Wiley-Interscience, New York
13. Fan W., Stolfo S.J., and Zhang J. The application of adaboost for distributed, scalable and on-line learning. KDD '99: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (1999), ACM, New York, NY, USA
14. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. EuroCOLT '95: Proceedings of the Second European Conference on Computational Learning Theory (1995), Springer, London, UK
15. Friedman J. Regularized discriminant analysis. Journal of the American Statistical Association 84 405 (1989) 165-175
16. Giannella C., Liu K., Olsen T., and Kargupta H. Communication efficient construction of decision trees over heterogeneously distributed data. Proceedings of the Fourth IEEE International Conference on Data Mining (2004)
17. Han J., and Kamber M. Data Mining: Concepts and Techniques (2005), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
18. Howland P., and Park H. Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 8 (2004) 995-1006
19. Kargupta H., and Park B.-H. A fourier spectrum-based approach to represent decision trees for mining data streams in mobile environments. IEEE Transactions on Knowledge and Data Engineering 16 2 (2004) 216-229
20. Kim H., and Loh W.-Y. Classification trees with unbiased multiway splits. Journal of the American Statistical Association 96 454 (2001) 589-604
21. Lazarevic A., and Obradovic Z. The distributed boosting algorithm. Knowledge Discovery and Data Mining (2001) 311-316
22. Lazarevic A., and Obradovic Z. Boosting algorithms for parallel and distributed learning. Distributed and Parallel Databases 11 2 (2002) 203-229
23. Lee J.-S., and Oh I.-S. Binary classification trees for multi-class classification problems. Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR'03) (2003)
24. Li X.-B., Sweigart J.R., Teng J.T.C., Donohue J.M., Thombs L.A., and Wang S.M. Multivariate decision trees using linear discriminants and tabu search. IEEE Transactions on Systems Man and Cybernetics 33 2 (2003) 195-205
25. Murthy S.K. Automatic construction of decision trees from data: a multi-disciplinary survey. Data Mining and Knowledge Discovery 2 4 (1998) 345-389
26. Murthy S.K., Kasif S., and Salzberg S. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research 2 (1994) 1-32
27. J. Ouyang, Survey of distributed decision tree induction techniques, Technical Report TR-OU-IIE-080912, Oakland University, 2008 〈http://iielab-secs.secs.okaland.edu/publications/TR-OU-IIE-080912.pdf〉.
28. Park B., Ayyagari R., and Kargupta H. A fourier analysis based approach to learning decision trees in a distributed environment. Proceedings of the First SIAM International Conference on Data Mining, Chicago, IL (2001)
29. Park C.H., and Park H. A comparison of generalized linear discriminant analysis algorithms. Pattern Recognition 41 3 (2008) 1083-1097
30. Pérez E., and Rendell L.A. Relational concepts and the fourier transform: an empirical study. AI '98: Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (1998), Springer, London, UK
31. A. Prodromidis, P. Chan, Meta-learning in distributed data mining systems: issues and approaches, Advances of Distributed Data Mining, MIT, AAAI Press, Cambridge, MA, 2000.
32. H. Qi, T. Wang, D. Birdwell, Global principal component analysis for dimensionality reduction in distributed data mining, Statistical Data Mining and Knowledge Discovery, CRC Press, Boca Raton, FL, 2004 (Chapter 19).
33. Quinlan J.R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
34. Quinlan J.R. C4.5: Programs for Machine Learning (1992), Morgan Kaufmann, Los Altos, CA
35. Sethi I.K., and Yoo J. Design of multicategory multifeature split decision trees using perceptron learning. Pattern Recognition 27 7 (1994) 939-947
36. Sethi I.K., and Yoo J.H. Structure-driven induction of decision tree classifiers through neural learning. Pattern Recognition 30 11 (1997) 1893-1904
37. Shah S., and Sastry P. New algorithms for learning and pruning oblique decision trees. IEEE Transactions on Systems Man and Cybernetics 29 4 (1999) 494-505
38. Raudys S., and Duin R.P.W. Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix. Pattern Recognition Letters 19 5-6 (1998) 385-392
39. Yang J., and Yang J. Why can LDA be performed in PCA transformed space?. Pattern Recognition 36 2 (2003) 563-566
40. Yildiz O.T., and Alpaydin E. Linear discriminant trees. Proceedings of the 17th International Conference on Machine Learning (2000), Morgan Kaufmann, San Francisco, CA
41. Yu H., and Yang J. A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recognition 34 (2001) 2067-2070