CaltechAUTHORS
  A Caltech Library Service

Feature subset selection using separability index matrix

Han, Jeong-Su and Lee, Sang-Wan and Bien, Zeungnam (2013) Feature subset selection using separability index matrix. Information Sciences, 223 . pp. 102-118. ISSN 0020-0255. doi:10.1016/j.ins.2012.09.042. https://resolver.caltech.edu/CaltechAUTHORS:20130206-144020987

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20130206-144020987

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/j.ins.2012.09.042DOIArticle
http://www.sciencedirect.com/science/article/pii/S0020025512006354PublisherArticle
ORCID:
AuthorORCID
Lee, Sang-Wan0000-0001-6266-9613
Additional Information:© 2012 Elsevier Inc. Received 1 July 2009; Received in revised form 23 November 2011; Accepted 26 September 2012; Available online 10 October 2012. This research was partly supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-C1090-1021-0010).
Funders:
Funding AgencyGrant Number
Ministry of Knowledge Economy (Korea)NIPA-2010-C1090-1021-0010
Subject Keywords:Feature subset selection; Filter method; Separability index matrix; EMG signal; Gait phase recognition
DOI:10.1016/j.ins.2012.09.042
Record Number:CaltechAUTHORS:20130206-144020987
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20130206-144020987
Official Citation:Jeong-Su Han, Sang Wan Lee, Zeungnam Bien, Feature subset selection using separability index matrix, Information Sciences, Volume 223, 20 February 2013, Pages 102-118, ISSN 0020-0255, 10.1016/j.ins.2012.09.042.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:36798
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:07 Feb 2013 23:30
Last Modified:09 Nov 2021 23:24

Repository Staff Only: item control page