ConfDTree: 一种基于统计的决策树改进方法

Gilad Katz, Asaf Shabtai, Lior Rokach, Nir Ofek

计算机科学技术学报 29 (3), 392-407, 2014

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.