A Comparative Study of Sequential Feature Selection Methods for Support Vector Machine

  • In this paper we investigate existing feature selection algorithms combined with support vector machine (SBS). Two ranking-based algorithms, recursive feature elimination (RFE) and incremental regularized risk minimization (IRRM), and greedy sequential backward search (SBS) are tested by using biosignal dataset which contains 35 features per sample and a total of 25 samples labeled by four emotion classes. The performance of the selection algorithms are compared by considering recognition rates obtained by the leave-one-out validation.

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Metadaten
Author:Jonghwa KimORCiD
URN:urn:nbn:de:bvb:384-opus4-4873
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/611
Series (Serial Number):Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2007-10)
Type:Report
Language:English
Date of Publication (online):2008/01/16
Publishing Institution:Universität Augsburg
Release Date:2008/01/16
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik

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