A social-spider optimization approach for support vector machines parameters tuning
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The choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we introduce a recently developed nature-inspired optimization algorithm to find out suitable values for SVM kernel mapping named Social-Spider Optimization (SSO). We compare the results obtained by SSO against with a Grid-Search, Particle Swarm Optimization and Harmonic Search. Statistical evaluation has showed SSO can outperform the compared techniques for some sort of kernels and datasets.
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Evolutionary Computing, Social-Spider Optimization, Support Vector Machines
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IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - SIS 2014: 2014 IEEE Symposium on Swarm Intelligence, Proceedings, p. 8-13.
