Normalized Mutual Information Feature Selection

被引：869

作者：

Estevez, Pablo. A. ^{[1
]}

Tesmer, Michel ^{[2
]}

Perez, Claudio A. ^{[1
]}

Zurada, Jacek A. ^{[3
]}

机构：

[1] Univ Chile, Dept Elect Engn, Santiago 8370451, Chile

[2] Cruz Blanca SA, Santiago, Chile

[3] Univ Louisville, Dept Comp & Elect Engn, Louisville, KY 40208 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2009年 / 20卷 / 02期

关键词：

Feature selection; genetic algorithms; multilayer perceptron (MLP) neural networks; normalized mutual information (MI); GENETIC ALGORITHMS; RELEVANCE;

D O I：

10.1109/TNN.2008.2005601

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：189 / 201

页数：13

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