An initial comparison on noise resisting between crisp and fuzzy decision trees

Abstract

Decision tree induction is an effective method to solve classification problem in machine learning domain. In general, there are two types of decision tree induction, i.e., crisp decision trees and fuzzy decision trees. Both decision tree inductions based on real-world data are unlikely to find the entirely accurate training set. This means noise existing in the training set. It should be noted that the noise can either cause attributes to become inadequate, or make the decision tree more complicated. It is necessary to further investigate decision trees where the influence of noise data is considered. Experimentally, the paper analyzes the effect of three types of noises, compares the tolerance capability of noise between fuzzy decision trees and crisp decision trees, discusses the modified degree of pruning methods in both fuzzy and crisp decision trees, and addresses the adjustable capability on noise by using different fuzzy reasoning operators in the fuzzy decision tree. Finally the empirical results show fuzzy decision tree is more robust than the crisp decision tree and the post-pruning crisp decision tree.