Feature subset selection using separability index matrix

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Effective Feature Subset Selection (FSS) is an important step when designing engineering systems that classify complex data in real time. The electromyographic (EMG) signal-based walking assistance system is a typical system that requires an efficient computational architecture for classification. The performance of such a system depends largely on a criterion function that assesses the quality of selected feature subsets. However, many well-known conventional criterion functions use less relevant features for classification or they have a high computational cost. Here, we propose a new criterion function that provides more effective FSS. The proposed criterion function, known as a separability index matrix (SIM), provides features pertinent to the classification task and a very low computational cost. This new function produces to a simple feature selection algorithm when combined with the forward search paradigm. We performed extensive experimental comparisons in terms of classification accuracy and computational costs to confirm that the proposed algorithm outperformed other filter-type feature selection methods that are based on various distance measures, including inter-intra, Euclidean, Mahalanobis, and Bhattacharyya distances. We then applied the proposed method to a gait phase recognition problem in our EMG signal-based walking assistance system. We demonstrated that the proposed method performed competitively when compared with other wrapper-type feature selection methods in terms of class-separability and recognition rate. (C) 2012 Elsevier Inc. All rights reserved.
Publisher
ELSEVIER SCIENCE INC
Issue Date
2013-02
Language
English
Article Type
Article
Citation

INFORMATION SCIENCES, v.223, pp.102 - 118

ISSN
0020-0255
DOI
10.1016/j.ins.2012.09.042
URI
http://hdl.handle.net/10203/207227
Appears in Collection
BiS-Journal Papers(저널논문)
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