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A filter model for feature subset selection based on genetic algorithm
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DOI：10.1016/j.knosys.2009.02.006.png)
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Abstract
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This paper describes a novel feature subset selection algorithm, which utilizes a genetic algorithm (CA) to optimize the output nodes of trained artificial neural network (ANN). The new algorithm does not depend on the ANN training algorithms or modify the training results. The two groups of weights between input-hidden and hidden-output layers are extracted after training the ANN on a given database. The general formula for each output node (class) of ANN is then generated. This formula depends only on input features because the two groups of weights are constant. This dependency is represented by a non-linear exponential function. The CA is involved to find the optimal relevant features, which maximize the output function for each class. The dominant features in all classes are the features subset to be selected from the input feature group. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Feature subset selection
Relevant feature
Genetic algorithm
Artificial neural networks
Non-linear optimization
Fitness function
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