Fuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection

DOI

10.2991/ijcis.2011.4.4.18How to use a DOI?

Keywords

Feature selection; fuzzy mutual information; redundancy; relevance; stability

Abstract

Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information. In this paper we introduce the fuzzy information entropy and fuzzy mutual information for computing relevance between numerical or fuzzy features and decision. The relationship between fuzzy information entropy and differential entropy is also discussed. Moreover, we combine fuzzy mutual information with "min-Redundancy-Max-Relevance", "Max-Dependency" and min-Redundancy-Max-Dependency" algorithms. The performance and stability of the proposed algorithms are tested on benchmark data sets. Experimental results show the proposed algorithms are effective and stable.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Daren Yu
AU  - Shuang An
AU  - Qinghua Hu
PY  - 2011
DA  - 2011/06/01
TI  - Fuzzy Mutual Information Based min-Redundancy and Max-Relevance Heterogeneous Feature Selection
JO  - International Journal of Computational Intelligence Systems
SP  - 619
EP  - 633
VL  - 4
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.4.18
DO  - 10.2991/ijcis.2011.4.4.18
ID  - Yu2011
ER  -