VQTree: Vector quantization for decision tree induction

Abstract

We describe a new oblique decision tree induction algorithm. The VQTree algorithm uses Learning Vector Quantization to form a nonparametric model of the training set, and from that obtains a set of hyperplanes which are used as oblique splits in the nodes of a decision tree. We use a set of public data sets to compare VQTree with two existing decision tree induction algorithms, 05.0 and OC1. Our experiments show that VQTree produces compact decision trees with higher accuracy than either C5.0 or OC1 on some datasets.