Cost-sensitive decision trees with post-pruning and competition for numeric data

Journal of Computational Information Systems

Volumn 9, Issue 20, 2013, Pages 7991-8001

  Xu, Zilong ^a   Min, Fan ^a   Zhu, William ^a  

^a MINNAN NORMAL UNIVERSITY   (China)

Author keywords

C4.5; Competition strategy; Cost sensitive decision tree; Numeric data; Post pruning

Indexed keywords

C4.5; COMPETITION STRATEGY; COST-SENSITIVE; NUMERIC DATA; POST-PRUNING;

DATA MINING; DECISION TREES; HEURISTIC ALGORITHMS; LEARNING ALGORITHMS;

COSTS;

EID: 84887311463     PISSN: 15539105     EISSN: None     Source Type: Journal    
DOI: 10.12733/jcis6488     Document Type: Article

References (24)

1. L. Breiman, Bagging predictors, Machine Learning 24 (2) (1996) 123-140.
2. L. Breiman, J. Friedman, C. Stone, R. Olshen, Classification and Regression Trees, Belmont CA: Wadsworth International Group, 1984.
3. W. Fan, S. Stolfo, J. Zhang, P. Chan, Adacost: Misclassification cost-sensitive boosting, in: Proceedings of the 16th International Conference on Machine Learning, 1999.
4. Y. Freund, R. Schapire, A desicion-theoretic generalization of on-line learning and an application to boosting, in: Computational learning theory, Springer, 1995.
5. E. B. Hunt, J. Marin, P. J. Stone (eds.), Experiments in induction, Academic Press, New York, 1966.
6. L. Jiang, C. Li, An empirical study on attribute selection measures in decision tree learning, Journal of Computational Information Systems 6 (1) (2010) 105-112.
7. F. Min, H. He, Y. Qian, W. Zhu, Test-cost-sensitive attribute reduction, Information Sciences 181 (2011) 4928-4942.
8. F. Min, W. Zhu, Minimal cost attribute reduction through backtracking, in: Proceedings of International Conference on Database Theory and Application, vol. 258 of FGIT-DTA/BSBT, CCIS, 2011.
9. F. Min, W. Zhu, A competition strategy to cost-sensitive decision trees, Rough Sets and Knowledge Technology (2012) 359-368.
10. F. Min, W. Zhu, H. Zhao, G. Pan, Coser: Cost-senstive rough sets, http://grc.fjzs.edu.cn/_fmin/coser/ (2011).
11. T. M. Mitchell, Machine Learning, McGraw-Hill Companies, 1997.
12. S. Norton, Generating better decision trees, in: Proceedings of the 11th International Joint Conference on Artificial Intelligence, 1989.
13. M. Núñez, The use of background knowledge in decision tree induction, Machine Learning 6 (1991) 231-250.
14. Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences 11 (1982) 341-356.
15. J. Quinlan, Induction of decision trees, Machine learning 1 (1) (1986) 81-106.
16. J. Quinlan, Bagging, boosting, and c4.5, in: Proceedings of the National Conference on Artificial Intelligence, 1996.
17. J. R. Quinlan, C4.5 Programs for Machine Learning, Morgan kaufmann Publisher, San Mateo, California, 1993.
18. R. E. Schapire, A brief introduction to boosting, in: Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
19. A. Skowron, On topology in information system, Bull. Polish Acad. Sci. Math. 36 (1988) 477-480.
20. M. Tan, Cost-sensitive learning of classification knowledge and its applications in robotics, Machine Learning 13 (1) (1993) 7-33.
21. P. D. Turney, Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research 2 (1995) 369-409.
22. X. Wu, V. Kumar, The top ten algorithms in data mining, vol. 9, Chapman and Hall/CRC, 2009.
23. Z. Xu, F. Min, W. Zhu, Ant colony optimization to minimal test cost reduction, in: Proceedings of IEEE International Conference on Granular Computing, 2012.
24. W. Zhu, F. Wang, Reduction and axiomization of covering generalized rough sets, Information Sciences 152 (1) (2003) 217-230.