A new mutual information based measure for feature selection

Al-Ani, A; Deriche, M; Chebil, J

A new mutual information based measure for feature selection

Al-Ani, A

Deriche, M Chebil, J

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Publication Type:: Journal Article
Citation:: Intelligent Data Analysis, 2003, 7 (1), pp. 43 - 57
Issue Date:: 2003-12-01

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Field	Value	Language
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Deriche, M	en_US
dc.contributor.author	Chebil, J	en_US
dc.date.issued	2003-12-01	en_US
dc.identifier.citation	Intelligent Data Analysis, 2003, 7 (1), pp. 43 - 57	en_US
dc.identifier.issn	1088-467X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8945
dc.description.abstract	In this paper, we discuss the problem of feature selection and the importance of using mutual information in evaluating the discrimination ability of feature subsets between class labels. Because of the difficulties associated with estimating the exact value of mutual information, we propose a new evaluation measure that is based on the information gain and takes into consideration the interaction between features. The proposed measure is integrated into a robust feature selection scheme and compared with the well-known mutual information feature selection (MIFS) algorithm using the problems of texture classification, speech segment classification and speaker identification. © 2003-IOS Press. All rights reserved.	en_US
dc.relation.ispartof	Intelligent Data Analysis	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A new mutual information based measure for feature selection	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	7	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

In this paper, we discuss the problem of feature selection and the importance of using mutual information in evaluating the discrimination ability of feature subsets between class labels. Because of the difficulties associated with estimating the exact value of mutual information, we propose a new evaluation measure that is based on the information gain and takes into consideration the interaction between features. The proposed measure is integrated into a robust feature selection scheme and compared with the well-known mutual information feature selection (MIFS) algorithm using the problems of texture classification, speech segment classification and speaker identification. © 2003-IOS Press. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/8945