Multiple neural networks using the reduced input dimension

Abstract

An ensemble of neural networks with competitive learning and consensus schemes is proposed. Conventional learning methods utilize all the dimensions of the original input patterns. However, a particular attribute of the input patterns does not necessarily contribute to classification. In this paper, we use the reduced input dimension for learning a neural network. We have developed three consensus schemes so as to judge the classification using multiple neural networks. The experimental results with remote sensing data indicate the improved performance of the networks when applying the proposed method to the conventional competitive learning algorithms. >