Top-Down Induction of Fuzzy Pattern Trees

Abstract

Fuzzy pattern tree induction was recently introduced as a novel machine learning method for classification. Roughly speaking, a pattern tree is a hierarchical, tree-like structure, whose inner nodes are marked with generalized (fuzzy) logical operators and whose leaf nodes are associated with fuzzy predicates on input attributes. A pattern-tree classifier is composed of an ensemble of such pattern trees: one for each class label. This type of classifier is interesting for several reasons. For example, since a single pattern tree can be considered as a kind of logical description of a class, it is quite appealing from an interpretation point of view. Moreover, in terms of classification accuracy, the method has shown promising performance in first experimental studies. Yet, as will be argued in this paper, the algorithm that has originally been proposed for learning fuzzy pattern trees from data offers scope for improvement. Here, we propose a new method that modifies the original proposal in several ways. Notably, our learning algorithm reverses the direction of pattern tree construction from bottom-up to top-down. Additionally, a different termination criterion is proposed that is more adapted to the learning problem at hand. Experimentally, it will be shown that our approach is indeed able to outperform the original learning method in terms of predictive accuracy.