Combining multiple neural networks for classification based on rough set reduction

Abstract

Generalization ability is a measure of performance of neural networks. Multiple neural networks combination based on the combination of a set of networks is used to achieve high pattern recognition performance. In our work rough set theory is introduced to reduce high dimensional data and get multiple concise representations (reducts) of a single sample set. Multiple neural networks classifiers are built based on different reducts. Average strategy and majority voting strategy are introduced to combine the outputs from different classifiers. The experimental results show the combined system outperforms a single classifier.