Relaxing instance boundaries for the search of splitting points of numerical attributes in classification trees

Information Sciences

Volumn 177, Issue 5, 2007, Pages 1276-1289

  Yen, Ester ^a   Chu, I Wen Mike ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Classification tree; Data mining; Decision trees; Heuristic partition method; Numerical attributes; Relaxation of instance boundaries; Splitting point

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DECISION THEORY; HEURISTIC METHODS; TREES (MATHEMATICS);

CLASSIFICATION TREE; DECISION TREES; HEURISTIC PARTITION METHOD; NUMERICAL ATTRIBUTES; RELAXATION OF INSTANCE BOUNDARIES; SPLITTING POINT;

DATA MINING;

EID: 33845400217     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2006.08.014     Document Type: Article

References (36)

1. Berzal F., Cubero J.C., Mari{dotless}́n N., and Sánchez D. Building multi-way decision trees with numerical attributes. Information Sciences 165 (2004) 73-90
2. C.L. Blake, C.J. Merz, UCI Repository of Machine Learning Databases. Available from: , UC Irvine, Department of Information and Computer Science, 1998.
3. Breiman L., et al. Classification and Regression Trees (1984), Wadsworth, Belmont
4. T.Y. Chen, P.L. Poon, Classification-hierarchy table: a methodology for construction of the classification tree, in: Proceedings of Australian Software Engineering Conference, 1996, pp. 93-104.
5. Chen T.Y., Poon P.L., and Tse T.H. A new restructuring algorithm for the classification-tree method. Proceedings of 9th International Workshop on Software Technology and Engineering Practice (1999), IEEE Computer Society, Los Alamitos, CA 105-114
6. Chen T.Y., Poon P.L., and Tse T.H. Classification-tree restructuring methodologies: a new perspective. IEEE Proceedings: Software 149 2 (2002) 65-74
7. M.J. Chu, The Classification of Continuous Attributes in Decision Tree Learning, Master thesis, Information and Computer Engineering, Chung Yuan Christian University, Taiwan, 1996.
8. Clark L.A., and Pregibon D. Tree-based models. In: Chambers J.M., and Hastie T.J. (Eds). Statistical Models in S (1992), Wadsworth Brooks/Cole, Pacific Grove, CA
9. D.G.T. Denison, B.K. Malick, Classification trees, in: Dey, Ghosh, Mallick (Eds.), Generalized Linear Models: A Bayesian perspective, 2000, pp. 365-372.
10. Dobra A., and Gehrke J. Bias correction in classification tree construction. Proceedings of the Seventeenth International Conference on Machine Learning (2001), Morgan Kaufman 90-97
11. Dougherty J., Kohavi R., and Sahami M. Supervised and unsupervised discretization of continuous features. Proceedings of the 12th International Conference on Machine Learning (1995), Morgan Kaufman, Los Altos, CA 194-202
12. Fayyad U.M., and Irani K.B. What should be minimized in a decision tree?. Proceedings of the Eighth National Conference on Artificial Intelligence AAAI-90 (1990), MIT Press, Cambridge, MA 749-754
13. Fayyad U.M., and Irani K.B. On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8 (1992) 87-102
14. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1993, pp. 1022-1027.
15. Fayyad U.M. From Data Mining to Knowledge Discovery: An Overview Advances in Knowledge Discovery and Data Mining (1993), AAAI Press
16. J. Gehrke, R. Ramakrishnan, V. Ganti, RainForest - a framework for fast decision tree construction of large datasets, in: Proceedings of the 24th VLDB Conference, New York, USA, 1998, pp. 416-427.
17. Han J., and Kamber M. Data Mining: Concepts and Techniques (2001), Morgan Kaufman
18. Hearst M.A. Support vector machines. IEEE Intelligent Systems (1998) 18-28
19. D.M. Hawkins, Firm: Formal inference based recursive modeling, Technical Report, University of Minnesota, 1997.
20. Hothorn T., and Lausen B. Bundling classifiers by bagging trees. Computational Statistics and Data Analysis 49 (2005) 1068-1078
21. Kim H., and Loh W.-Y. Classification trees with unbiased multiway splits. Journal of the American Statistical Association 96 (2001) 589-604
22. Kurgan L., and Cios K. CAIM discretization algorithm. IEEE Transactions on Knowledge and Data Engineering 16 2 (2004) 145-153
23. R.J. Lewis, An introduction to classification and regression tree (CART) analysis, The Annual Meeting of the Society for Academic Emergency Medicine, Francisco, California, 2000.
24. Liu H., Hussain F., Tan C.L., and Dash M. Discretization: an enabling technique. Journal of Data Mining and Knowledge Discovery 6 4 (2002) 393-423
25. M. Mehta, R. Agrawal, J. Rissanen, SLIQ: a fast scalable classifier for data mining, in: Proceedings of the 5th International Conference on Extending Database Technology, Avignon, France, 1996, pp. 18-32.
26. D. Pavlov, J. Mao, B. Dom, Scaling-up support vector machines using boosting algorithm, in: International Conference on Pattern Recognition (ICPR'00), vol. 2, 2000.
27. Perner P., and Trautzsch S. Multi-interval discretization methods for decision tree learning. In: Amin A., Dori D., Pudil P., and Freeman H. (Eds). Advances in Pattern Recognition. LNCS 1451 (1998), Springer-Verlag 475-482
28. Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
29. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufman
30. Quinlan J.R. Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research 4 (1996) 77-90
31. J.C. Shafer, R. Agrawal, M. Mehta, SPRINT: a scalable parallel classifier for data mining, in: Proceedings of 22nd International Conference on VLDB, Mumbai (Bombay), India, 1996, pp. 544-555.
32. Shih Y.S. Selecting the best splits for classification trees with categorical variables. Statistics and Probability Letters 54 (2001) 341-345
33. Shih Y.S. Regression trees with unbiased variable selection. Statistica Sinica 12 (2002) 361-386
34. Shih Y.S. A note on split selection bias in classification trees. Computational Statistics and Data Analysis 45 (2004) 457-466
35. Wu X., and Urpani D. Induction by attribute elimination. IEEE Transactions on Knowledge and Data Engineering 11 5 (1999) 805-812
36. Zhang S., and Wu X. Large scale data mining based on data partitioning. Applied Artificial Intelligence 15 2 (2001) 129-139