期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 20, 期 2, 页码 189-201出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2008.2005601
关键词
Feature selection; genetic algorithms; multilayer perceptron (MLP) neural networks; normalized mutual information (MI)
类别
资金
- Conicyt-Chile
- Fondecyt [1050751, 7060211]
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 date sets without requiring a user-defined parameter. In addition, MIFS 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.
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