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. Index Terms-Feature selection, genetic algorithms, multilayer perceptron (MLP) neural networks, normalized mutual information (MI).
A normalized mutual information feature selection (NMIFS) , has been used as a fitness function in a GA based filter method REF .
Normalized Mutual Information Feature Selection
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Medicine Computer Science