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

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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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  -