A comparison of mutual and fuzzy-mutual information-based feature selection strategies

Yu Shuen Tsai, Ueng Cheng Yang, I. Fang Chung, Chuen Der Huang

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

14 Scopus citations

Abstract

It is very important to select a small set of relevant features from a high dimensional data set and useful to design either an effective classification or prediction model. This procedure involves a series of estimations of the relationship between each pair of variables and between each variable and class labels. Mutual information is widely used to estimate these relationships. However, alternative strategies may be useful to estimate the mutual information with continuous or hybrid data. In this study, we attempt to evaluate the difference between the selection strategies involved with mutual information and fuzzy mutual information. The results indicate that using fuzzy mutual information is more helpful to obtain more stable feature sets and more accurate estimations of the relationship between two variables.

Original languageEnglish
Title of host publicationFUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India
Duration: 7 Jul 201310 Jul 2013

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
Country/TerritoryIndia
CityHyderabad
Period7/07/1310/07/13

Keywords

  • Feature selection
  • Fuzzy mutual information
  • Mutual information
  • Symmetric uncertainty

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