A new wrapper feature selection approach using neural network

This paper presents a new feature selection (FS) algorithm based on the wrapper approach using neural networks (NNs). The vital aspect of this algorithm is the automatic determination of NN architectures during the FS process. Our algorithm uses a constructive approach involving correlation information in selecting features and determining NN architectures. We call this algorithm as constructive approach for FS (CAFS). The aim of using correlation information in CAFS is to encourage the search strategy for selecting less correlated (distinct) features if they enhance accuracy of NNs. Such an encouragement will reduce redundancy of information resulting in compact NN architectures. We evaluate the performance of CAFS on eight benchmark classification problems. The experimental results show the essence of CAFS in selecting features with compact NN architectures.