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Normalized mutual information feature selection (2009)
| Venue: | IEEE TRANS. NEURAL NETW. |
| Citations: | 31 - 1 self |
BibTeX
@ARTICLE{Estévez09normalizedmutual,
author = {Pablo A. Estévez and Michel Tesmer and Claudio A. Perez and Jacek M. Zurada},
title = {Normalized mutual information feature selection},
journal = {IEEE TRANS. NEURAL NETW. },
year = {2009},
volume = {20},
number = {2},
pages = {189--201}
}
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Abstract
A filter method of feature selection based on mutual information, called normalized mutual information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti’s MIFS, MIFS-U, and mRMR methods. The average normalized mutual information is proposed as a measure of redundancy among features. NMIFS outperformed MIFS, MIFS-U, and mRMR on several artificial and benchmark data sets without requiring a user-defined parameter. In addition, NMIFS is combined with a genetic algorithm to form a hybrid filter/wrapper method called GAMIFS. This includes an initialization procedure and a mutation operator based on NMIFS to speed up the convergence of the genetic algorithm. GAMIFS overcomes the limitations of incremental search algorithms that are unable to find dependencies between groups of features.
Keyphrases
mutual information feature selection genetic algorithm normalized mutual information feature selection initialization procedure filter method average normalized mutual information benchmark data set mutation operator incremental search algorithm user-defined parameter hybrid filter wrapper method feature selection mutual information mrmr method battiti mifs
