Abstract
A hybrid feature selection algorithm based on least squares support vector machine (LSSVM) and discrete particle swarm optimization is proposed in this paper. The proposed algorithm takes advantage of the easy solving of LSSVM, adopts LSSVM to construct classifier, and use accuracy as the main part of fitness function on the process of particle swarm optimization. The simulation results show that the proposed algorithm could obtain the features which contribute a lot to classifier. Therefore the dimension of data is decreased and the efficiency of classifier is improved.
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Chuyi, S., Jingqing, J., Chunguo, W., Yanchun, L. (2011). Feature Selection Algorithm Based on Least Squares Support Vector Machine and Particle Swarm Optimization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_33
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DOI: https://doi.org/10.1007/978-3-642-21524-7_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21523-0
Online ISBN: 978-3-642-21524-7
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