Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.asoc.2004.02.001
Title: Feature selection for modular GA-based classification
Authors: Zhu, F.
Guan, S. 
Keywords: Class decomposition
Classification
Feature selection
Genetic algorithm
Issue Date: Sep-2004
Source: Zhu, F., Guan, S. (2004-09). Feature selection for modular GA-based classification. Applied Soft Computing Journal 4 (4) : 381-393. ScholarBank@NUS Repository. https://doi.org/10.1016/j.asoc.2004.02.001
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. © 2004 Elsevier B.V. All rights reserved.
Source Title: Applied Soft Computing Journal
URI: http://scholarbank.nus.edu.sg/handle/10635/56033
ISSN: 15684946
DOI: 10.1016/j.asoc.2004.02.001
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