Feature Selection using a Novel Particle Swarm Optimization and It’s Variants
Автор: R. Parimala, R. Nallaswamy
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 5 Vol. 4, 2012 года.
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Feature selection has been keen area of research in classification problem. Most of the researchers mainly concentrate on statistical measures to select the feature subset. These methods do not provide a suitable solution because the search space increases with the feature size. The FS is a very popular area for applications of population-based random techniques. This paper suggests swarm optimization technique, binary particle swarm optimization technique and its variants, to select the optimal feature subset. The main task of the BPSO is the selection of the features used by the SVM in the classification of spambase data set. The results of our experiments show a very strong relation between number of features and accuracy. Comparison of the optimized results and the un-optimized results showed that the BPSO-MS method could significantly reduce the computation cost while improving the classification accuracy.
Feature selection, Support Vector Machine, Particle Swarm optimization
Короткий адрес: https://sciup.org/15011685
IDR: 15011685
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