Improving the readability of decision trees using reduced complexity feature extraction

Abstract

Understandability of decision trees depends on two key factors: the size of the trees and the complexity of their node functions. Most of the attempts to improve the behavior of decision trees have been focused only on reducing their sizes by building the trees on complex features. These features are usually linear or non-linear functions of all the original attributes. In this paper, reduced complexity features are proposed as a way to reduce the size of decision trees while keeping understandable functions at their nodes. The proposed approach is tested on a robot grasping application where the goal is to obtain a system able to classify grasps as valid or invalid and also on three datasets from the UCI repository. © Springer-Verlag Berlin Heidelberg 2005.