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

Gan, JQ and Awwad Shiekh Hasan, B and Tsui, CSL (2014) 'A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space.' International Journal of Machine Learning and Cybernetics, 5 (3). 413 - 423. ISSN 1868-8071

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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. © 2012 Springer-Verlag Berlin Heidelberg.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 03 Dec 2014 11:09
Last Modified: 23 Jan 2019 00:16
URI: http://repository.essex.ac.uk/id/eprint/11961

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