MIKM: A mutual information-based K-medoids approach for feature selection

Jung Yi Jiang, Yao Lung Su, Shie Jue Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

We propose a mutual information-based K-medoids approach (MIKM) for unsupervised and supervised feature selection, MIKM adopts mutual information (MI) to measure similarity between two features and applies K-medoids clustering to find representatives of feature clusters. The method partitions the original feature set into some distinct subsets or clusters such that the features within a cluster are highly similar to each other while those in different clusters are dissimilar, Each obtained representative is one of the original features. Consequently, The obtained representatives form a subset of the original features. Experimental results show that our proposed method can work more effectively than other methods.

Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages102-107
Number of pages6
DOIs
Publication statusPublished - 2011 Nov 7
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 2011 Jul 102011 Jul 13

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume1
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
CountryChina
CityGuilin, Guangxi
Period11-07-1011-07-13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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    Jiang, J. Y., Su, Y. L., & Lee, S. J. (2011). MIKM: A mutual information-based K-medoids approach for feature selection. In Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 (pp. 102-107). [6016694] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 1). https://doi.org/10.1109/ICMLC.2011.6016694