Neural trees: a new tool for classification

Abstract

The authors propose a new classifier based on neural network techniques. The ‘network’ consists of a set of perceptrons functionally organized in a binary tree (‘neural tree’). The learning algorithm is inspired from a growth algorithm, the tiling algorithm, recently introduced for feedforward neural networks. As in the former case, this is a constructive algorithm, for which convergence is guaranteed. In the neural tree one distinguishes the structural organization from the functional organization: each neuron of a neural tree receives inputs from, and only from, the input layer; its output does not feed into any other neuron, but is used to propagate down a decision tree. The main advantage of this approach is due to the local processing in restricted portions of input space, during both learning and classification. Moreover, only a small subset of neurons have to be updated during the classification stage. Finally, this approach is easily and efficiently extended to classification in a multiclass problem. Promising numerical results have been obtained on different two- and multiclass problems (parity problem, multiclass nearest-neighbour classification task, etc.) including a ‘real’ low-level speech processing task. In all studied cases results compare favourably with the traditional ‘back propagation’ approach, in particular on learning and classification times as well as on network size.