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article

Feature subset selection using separability index matrix

Authors:

Zeungnam BienAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 223

Pages 102 - 118

https://doi.org/10.1016/j.ins.2012.09.042

Published: 01 February 2013 Publication History

Abstract

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.

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Cited By

Wu YLiang SMa YLi B(2024)Prediction and classification of sEMG-based pinch force between different fingersExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121635237:PCOnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121635
Chen C(2019)Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighborsInformation Sciences: an International Journal10.1016/j.ins.2015.05.019318:C(14-27)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2015.05.019
Liu HZhang SWu X(2019)MLSLRInformation Sciences: an International Journal10.1016/j.ins.2014.05.013281(310-320)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2014.05.013
Show More Cited By

Feature subset selection using separability index matrix
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning algorithms

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 223, Issue

February, 2013

335 pages

ISSN:0020-0255

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2012.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 February 2013

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Cited By

Wu YLiang SMa YLi B(2024)Prediction and classification of sEMG-based pinch force between different fingersExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121635237:PCOnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121635
Chen C(2019)Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighborsInformation Sciences: an International Journal10.1016/j.ins.2015.05.019318:C(14-27)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2015.05.019
Liu HZhang SWu X(2019)MLSLRInformation Sciences: an International Journal10.1016/j.ins.2014.05.013281(310-320)Online publication date: 6-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2014.05.013
Huang QYang FLiu LLi X(2015)Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosisInformation Sciences: an International Journal10.1016/j.ins.2014.08.021314:C(293-310)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.08.021
Zhang SYao HSun XWang KZhang JLu XZhang Y(2014)Action recognition based on overcomplete independent components analysisInformation Sciences: an International Journal10.1016/j.ins.2013.12.052281(635-647)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.ins.2013.12.052

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