Tree - Decision Trees for Tree Structured Data

Björn Bringmann, Albrecht Zimmermann

Abstract

We present Tree2, a new approach to structural classification. This integrated approach induces decision trees that test for pattern occurrence in the inner nodes. It combines state-of-the-art tree mining with sophisticated pruning techniques to find the most discriminative pattern in each node. In contrast to existing methods, Tree2 uses no heuristics and only a single, statistically well founded parameter has to be chosen by the user. The experiments show that Tree2 classifiers achieve good accuracies while the induced models are smaller than those of existing approaches, facilitating better comprehensibility.