Feature selection based on neuro-fuzzy networks

Abstract

Feature selection algorithm based on artificial neural networks can be taken as a special case of architecture pruning algorithm: compute the sensitivity of network outputs against pruned features. However, these methods usually require preprocessing of data normalization, which will possibly change original data's characters that are important to classification. Neuro-fuzzy (NF) network is a fuzzy inference system (FIS) with self-study ability. We combine it with architecture pruning algorithm based on membership space and propose a new feature selection algorithm. Finally, experiments using both natural and integrated data are carried out and compared with other methods. The results approve the validity of the algorithm.