›› 2014, Vol. 29 ›› Issue (3): 392-407.doi: 10.1007/s11390-014-1438-5

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Data Management and Data Mining • Previous Articles     Next Articles

ConfDTree:Statistical Methods for Improving Decision Trees

Gilad Katz, Asaf Shabtai, Lior Rokach, and Nir Ofek   

  1. Department of Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel Telekom Innovation Laboratories, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
  • Received:2013-09-08 Revised:2014-01-27 Online:2014-05-05 Published:2014-05-05
  • About author:Gilad Katz is a Ph.D. student at the Department of Information Systems Engineering at Ben-Gurion University of the Negev. He received both his B.Sc. and M.Sc. degrees from this department. His main areas of interest include text mining, machine learning and big data. For the past four years, Gilad has also worked as a researcher at Deutsche Telekom Labs at Ben-Gurion University.

Decision trees have three main disadvantages: reduced performance when the training set is small; rigid decision criteria; and the fact that a single "uncharacteristic" attribute might "derail" the classification process. In this paper we present ConfDTree (Confidence-Based Decision Tree)——a post-processing method that enables decision trees to better classify outlier instances. This method, which can be applied to any decision tree algorithm, uses easy-to-implement statistical methods (confidence intervals and two-proportion tests) in order to identify hard-to-classify instances and to propose alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%~9% in the AUC performance is reported.

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