Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1480
Title: Feature selection for modular GA-based classification
Authors: Zhu, F
Guan, SU
Keywords: Classification;Feature selection;Genetic algorithm;Class decomposition
Issue Date: 2004
Publisher: Elsevier
Citation: Applied Soft Computing 4(4): 381-393, Sep 2004
Abstract: Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, Relative Importance Factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced.
URI: http://bura.brunel.ac.uk/handle/2438/1480
DOI: http://dx.doi.org/10.1016/j.asoc.2004.02.001
ISSN: 1568-4946
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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