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Model Selection in SVMs Using Differential Evolution
World Congress, Volume # 18 | Part# 1
Authors
Zhang, Jingjing; Niu, Qun; Li, Kang; Irwin, George W.
Digital Object Identifier (DOI)
10.3182/20110828-6-IT-1002.00584
Page Numbers:
14717-14722
Index Terms
Fuzzy and neural systems relevant to control and identification; Evolutionary algorithms in control and identification
Abstract
To improve the performance of classification using Support Vector Machines (SVMs) while reducing the model selection time, this paper introduces Differential Evolution, a heuristic method for model selection in two-class SVMs with a RBF kernel. The model selection method and related tuning algorithm are both presented. Experimental results from application to a selection of benchmark datasets for SVMs show that this method can produce an optimized classification in less time and with higher accuracy than a classical grid search. Comparison with a Particle Swarm Optimization (PSO) based alternative is also included.
References
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