A Social-Spider Optimization Approach for Support Vector Machines Parameters Tuning - Pereira, Danillo R.; Pazoti, Mario A.; Pereira, Luis A. M. [UNESP]; Papa, Joao Paulo [UNESP]; IEEE (Ieee, 2014)
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.
Keywords: Support Vector Machines; Social-Spider Optimization; Evolutionary Computing