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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2014

01.06.2014 | Original Article

A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space

verfasst von: John Q. Gan, Bashar Awwad Shiekh Hasan, Chun Sing Louis Tsui

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2014

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Abstract

Sequential forward floating search (SFFS) has been well recognized as one of the best feature selection methods. This paper proposes a filter-dominating hybrid SFFS method, aiming at high efficiency and insignificant accuracy sacrifice for high-dimensional feature subset selection. Experiments with this new hybrid approach have been conducted on five feature data sets, with different combinations of classifier and separability index as alternative criteria for evaluating the performance of potential feature subsets. The classifiers under consideration include linear discriminate analysis classifier, support vector machine, and K-nearest neighbors classifier, and the separability indexes include the Davies-Bouldin index and a mutual information based index. Experimental results have demonstrated the advantages and usefulness of the proposed method in high-dimensional feature subset selection.
Vorheriger Artikel Research on multi-objective linear programming problem with fuzzy coefficients in constraints
Nächster Artikel A two stages sparse SVM training

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Fußnoten
1
The challenge itself contains EEG signals from 7 subjects, recorded during synchronous BCI experiments. Three subjects are synthesized, thus we used only the data recorded from 4 human subjects.
 
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Metadaten
Titel
A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space
verfasst von
John Q. Gan
Bashar Awwad Shiekh Hasan
Chun Sing Louis Tsui
Publikationsdatum
01.06.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2014
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0139-z

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