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- W2113750481 abstract "In creating a pattern classifier, feature selection is often used to prune irrelevant and noisy features to producing effective features. Manually developing a feature set can be a very time consuming and costly endeavor. In this paper, an efficient feature selection algorithm based on improved binary particle swarm optimization and support vector machine Algorithm (IBPSO-SVM) was used. First a population of particles (feature subsets) were randomly generated, and then optimized by IBPSO-SVM wrapper algorithms; finally the best fitness feature subset was applied to SVM classification. The simulation experiment results have proved that the feature subset selection algorithm based on IBPSO-SVM is very effective." @default.
- W2113750481 created "2016-06-24" @default.
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- W2113750481 date "2009-12-01" @default.
- W2113750481 modified "2023-09-26" @default.
- W2113750481 title "Feature Subset Selection Based on Improved Discrete Particle Swarm and Support Vector Machine Algorithm" @default.
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- W2113750481 doi "https://doi.org/10.1109/iciecs.2009.5362705" @default.
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