Mutual information based input feature selection for classification problems

Shuang Cang*, Hongnian Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

The elimination process aims to reduce the size of the input feature set and at the same time to retain the class discriminatory information for classification problems. This paper investigates the approaches to solve classification problems of the feature selection and proposes a new feature selection algorithm using the mutual information (MI) concept in information theory for the classification problems. The proposed algorithm calculates the MI between the combinations of input features and the class instead of the MI between a single input feature and the class for both continuous-valued and discrete-valued features. Three experimental tests are conducted to evaluate the proposed algorithm. Comparison studies of the proposed algorithm with the previously published classification algorithms indicate that the proposed algorithm is robust, stable and efficient.

Original languageEnglish
Pages (from-to)691-698
Number of pages8
JournalDecision Support Systems
Volume54
Issue number1
Early online date24 Aug 2012
DOIs
Publication statusPublished - 1 Dec 2012

Fingerprint Dive into the research topics of 'Mutual information based input feature selection for classification problems'. Together they form a unique fingerprint.

Cite this