International Journal of Computational Intelligence Systems

Volume 9, Issue 4, August 2016, Pages 726 - 733

A Mutual Information estimator for continuous and discrete variables applied to Feature Selection and Classification problems

Authors
Frederico Coelho1, fredgfc@ufmg.br, Antonio P. Braga2, apbraga@ufmg.br, Michel Verleysen3, michel.verleysen@uclouvain.be
Received 23 March 2015, Accepted 11 April 2016, Available Online 1 August 2016.
DOI
10.1080/18756891.2016.1204120How to use a DOI?
Keywords
Feature Selection; Mutual Information; Classification
Abstract

Currently Mutual Information has been widely used in pattern recognition and feature selection problems. It may be used as a measure of redundancy between features as well as a measure of dependency evaluating the relevance of each feature. Since marginal densities of real datasets are not usually known in advance, mutual information should be evaluated by estimation. There are mutual information estimators in the literature that were specifically designed for continuous or for discrete variables, however, most real problems are composed by a mixture of both. There is, of course, some implicit loss of information when using one of them to deal with mixed continuous and discrete variables. This paper presents a new estimator that is able to deal with mixed set of variables. It is shown in experiments with synthetic and real datasets that the method yields reliable results in such circumstance.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 4
Pages
726 - 733
Publication Date
2016/08/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1204120How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Frederico Coelho
AU  - Antonio P. Braga
AU  - Michel Verleysen
PY  - 2016
DA  - 2016/08/01
TI  - A Mutual Information estimator for continuous and discrete variables applied to Feature Selection and Classification problems
JO  - International Journal of Computational Intelligence Systems
SP  - 726
EP  - 733
VL  - 9
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1204120
DO  - 10.1080/18756891.2016.1204120
ID  - Coelho2016
ER  -