A Growth Algorithm for Neural Network Decision Trees

Abstract

This paper explores the application of neural network principles to the construction of decision trees from examples. We consider the problem of constructing a tree of perceptrons able to execute a given but arbitrary Boolean function defined on Ni input bits. We apply a sequential (from one tree level to the next) and parallel (for neurons in the same level) learning procedure to add hidden units until the task in hand is performed. At each step, we use a perceptron-type algorithm over a suitable defined input space to minimize a classification error. The internal representations obtained in this way are linearly separable. Preliminary results of this algorithm are presented.