Improving Classification Using a Tree Structured Neural Network

Abstract

This article introduces a tree structured network for improving the performance of the feedforward neural network (FN) classifier. The building blocks of the tree are the feedforward neural network with backpropagation learning scheme and the simple logical OR neural network (ORNN). The confusion matrix (CM), resulting from some preliminary experiments, is used to divide the considered patterns into groups in several primary stages until no more grouping could be obtained. The proposed structure can be used for any pattern classification problem. In this article, the testing environment is the isolated handwritten Arabic character set, which is a problem of reasonable complexity. Two simple kinds of feature vectors are used to represent characters before the FN. The use of the proposed tree structure improved the recognition accuracy from 80% for the single FN to 97.7% (using the same simple set of features). The results show that with the proposed method, better classification results could be obtained without having to introduce more complex features.