Fuzzy min-max neural network based decision trees

Abstract

This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the flexibility inherent in the fuzzy logic and the computational efficiency of the min-max neural networks are combined in the decision tree learning framework. FMMDT splits the feature space non-linearly based on multiple attributes which provides not only conceptually more insightful splits but also decision trees with smaller size and depth. The decision trees resulted from the FMMDT learning algorithm have a non-traditional architecture, which enables determining the class label of the instances as early as possible. Moreover, FMMDT creates decision trees which are interpretable by the domain expert. It is shown experimentally that the decision trees resulted from the proposed FMMDT learning algorithm achieve the highest accuracy and the lowest size and depth in comparison with C4.5, BFTree, SimpleCart and NBTree on the most commonly used UCI data sets. Moreover, the experiments reveal that FMMDT creates decision trees with stable structure.