Journal
NEUROCOMPUTING
Volume 73, Issue 16-18, Pages 3273-3283Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2010.04.003
Keywords
Feature selection; Wrapper approach; Neural networks; Correlation information
Categories
Funding
- Japanese Society for Promotion of Sciences
- Yazaki Memorial Foundation for Science and Technology
- University of Fukui
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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. (C) 2010 Elsevier B.V. All rights reserved.
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