A Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning
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Ieee
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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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Support Vector Machines, Social-Spider Optimization, Evolutionary Computing
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Inglês
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2014 Ieee Symposium On Swarm Intelligence (sis). New York: Ieee, p. 8-13, 2014.
