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A discrete particle swarm optimization method for feature selection in binary classification problems
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DOI：10.1016/j.ejor.2010.02.032.png)
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This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance. (C) 2010 Elsevier B.V. All rights reserved.
Keyword:
Feature selection
Particle swarm optimization
Metaheuristics
Binary classification
Logistic regression
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6
论文数:
2.2W
被引数:
6.4W
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