Design of multicategory multifeature split decision trees using perceptron learning

Abstract

The decision tree methodology is an important nonparametric technique for building classifiers from a set of training examples. Most of the existing top-down decision tree design methods make use of single feature splits at successive stages of the tree design. While computationally attractive, single feature splits generally lead to large trees and inferior performance. This paper presents a new top-down decision tree design method that generates compact trees of superior performance by using multifeature splits in place of single feature splits at successive stages of the tree development. The multifeature splits in the proposed method are obtained by combining the concept of information measure of a partition with perceptron learning. Several decision tree induction results for a broad range of classification problems are presented to demonstrate the strengths of the proposed decision tree design methods.