ConfDTree: Improving decision trees using confidence intervals

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 339-348

  Katz, Gilad ^a   Shabtai, Asaf ^a   Rokach, Lior ^a   Ofek, Nir ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Confidence intervals; Decision trees; Imbalanced datasets

Indexed keywords

ALTERNATIVE ROUTES; CLASSIFICATION PROCESS; CONFIDENCE INTERVAL; DECISION CRITERIONS; EXPERIMENTAL STUDIES; IMBALANCED DATA-SETS; MULTI-CLASS; POSTPROCESSING METHODS; PREDICTIVE PERFORMANCE; TRAINING SETS; TREES ALGORITHM;

DATA MINING; FORESTRY;

DECISION TREES;

ALGORITHMS; DATA BASES; DECISION MAKING; FORESTRY;

EID: 84874104724     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.19     Document Type: Conference Paper

References (28)

1. Rokach, L. and O. Maimon, Data mining with decision trees: theory and applications. World Scientific Pub-lishing, Singapore, 2008.
2. Quinlan, J.R., C4. 5: programs for machine learning. Vol. 1. 1993: Morgan kaufmann.
3. Chawla, N.V., N. Japkowicz, and A. Kotcz, Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. Newsl., 2004. 6(1): p. 1-6.
4. Provost, F. and P. Domingos, Well-Trained PETs: Improving Probability Estimation Trees. Technical Report CDER #00-04-IS, 2001.
5. Lin, H.-Y., Efficient classifiers for multi-class classification problems. Decision Support Systems, 2012. 53(3): p. 473-481.
6. Breiman, L., Random Forests. Machine Learning, 2001. 45(1): p. 5-32.
7. Quinlan, J.R., Induction of decision trees. Machine Learning, 1986. 1(1): p. 81-106.
8. Quinlan, J.R., C4.5: Programs for Machine Learning. 1993.
9. Breiman, L., et al., Classification and regression trees. Belmont: Wadsworth, 1984.
10. Breiman, L., Technical Note: Some Properties of Splitting Criteria. Machine Learning, 1996. 24(1): p. 41-47.
11. Rodriguez, J.J., L.I. Kuncheva, and C.J. Alonso, Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006. 28(10): p. 1619-1630.
12. John, G.H., Robust Decision Trees: Removing Outliers from Databases. Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 1995: p. 174-179.
13. Last, M., O. Maimon, and E. Minkov, Improving Stability of Decision Trees. International Journal of Pattern Recognition and Artificial Intelligence, 2002: p. 145-159.
14. Zadrozny, B. and C. Elkan, Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. Proceedings of the Eighteenth International Conference on Machine Learning, 2001.
15. Chm-les X, L.C., O. CA, and R.J. Yan, Decision Tree with Better Ranking.
16. Mccallum, R.A., Instance-Based Utile Distinctions for Reinforcement Learning with Hidden StateR. Andrew Mccallum Proceedings of the Twelfth International Conference on Machine Learning, 1995: p. 387-395.
17. Massey, F.J., The Kolmogorov-Smirnov Test for Goodness of Fit. Journal of the American Statistical Association, 1951. 46(253): p. 68-78.
18. Janikow, C.Z., Fuzzy decision trees: issues and methods Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 1998. 28(1): p. 1-14.
19. Olaru, C. and L. Wehenkel, A complete fuzzy decision tree technique. Fuzzy Sets and Systems, 2003. 138(2): p. 221-254.
20. Zadorny, B. and C. Elkan, Obtaining calibrated probability estimates from decision trees and naive Bayesian classiers. In: Proc. Eighteenth Internat. Conf. on Machine Learning, 2001: p. 609-616.
21. Esposito, F., D. Malerba, and G. Semeraro, A comparative analysis of methods for pruning decision trees. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997. 19(5): p. 476-491.
22. Kohavi, R., B. Becker, and S. D, Improving Simple Bayes. The 9th European Conference on Machine Learning, 1997: p. 78-87.
23. Ponte, J.M. and W.B. Croft, A language modeling approach to information retrieval, in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval1998, ACM: Melbourne, Australia. p. 275-281.
24. Lafferty, J. and C. Zhai, Document language models, query models, and risk minimization for information retrieval, in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval2001, ACM: New Orleans, Louisiana, United States. p. 111-119.
25. Demšar, J. and ar, Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res., 2006. 7: p. 1-30.
26. Hand, D.J. and R.J. Till, A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems. Machine Learning, 2001. 45(2): p. 171-186.
27. Hall, M., et al., The WEKA Data Mining Software: An Update. SIGKDD Explorations, 2009. 11(1).
28. Bowyer, K.W., et al., SMOTE: Synthetic Minority Over-sampling Technique. Journal Of Artificial Intelligence Research, 2002. 16: p. 321-357.