A fuzzy matching method of fuzzy decision trees
Junhyeong Lee, Juan Sun, Lanzhen Yang · 2004
In this paper, we present a matching method that can improve the classification performance of a fuzzy decision tree (FDT). This method takes into consideration prediction strength of leave nodes of a fuzzy decision tree by combining true degrees (CF) of fuzzy rules, generated from a fuzzy decision tree, with membership degrees of antecedent parts of rules when applied to cases for classification. We illustrate the importance of CF through an example. An experiment shows by using this method, we can obtain more accurate results of classification when compared to the original method and to those obtained using the C5.0 decision tree.