A mutual information based feature selection algorithm

Shuang Cang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

The objective of the eliminating process is to reduce the size of the input feature set and at the same time to retain the class discriminatory information. This paper proposes and evaluates a new feature selection algorithm using information theory which is the mutual information (MI) between combinations of input features and the class instead of mutual information between a single input feature and the class for both continuous-valued and discrete-valued features. Comparison studies of new and previously published classification algorithms indicate that the proposed algorithm is robust, stable and efficient.

Original languageEnglish
Title of host publicationProceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011
PublisherIEEE
Pages2241-2245
Number of pages5
Volume4
ISBN (Print)9781424493524
DOIs
Publication statusPublished - 12 Dec 2011
Event2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011 - Shanghai, China
Duration: 15 Oct 201117 Oct 2011

Conference

Conference2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011
Country/TerritoryChina
CityShanghai
Period15/10/1117/10/11

Fingerprint

Dive into the research topics of 'A mutual information based feature selection algorithm'. Together they form a unique fingerprint.

Cite this