Abstract
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.
Original language | American English |
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Pages (from-to) | 392-407 |
Number of pages | 16 |
Journal | Journal of Computer Science and Technology |
Volume | 29 |
Issue number | 3 |
DOIs | |
State | Published - 1 Jan 2014 |
Keywords
- confidence interval
- decision tree
- imbalanced dataset
All Science Journal Classification (ASJC) codes
- Software
- Theoretical Computer Science
- Hardware and Architecture
- Computer Science Applications
- Computational Theory and Mathematics