Parameter selection of support vector regression machine based on differential evolution algorithm

Authors:
Qing Yu;Ying Liu;Feng Rao
Affiliations:
Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Tianjin Universit ...;Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Tianjin Universit ...;Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China and Key Laboratory of Computer Vision and System, Tianjin Universit ...
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Year:
2009

Citing 2
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Abstract

This parameters selection is an important issue in the research of Ɛ -support vector regression machine (Ɛ-SVRM), whose nature is an optimization selection process. Motivated by the effectiveness of Differential Evolution (DE) algorithm on optimization problem, a new automatic searching method based on DE algorithm was proposed. Experimental results demonstrate that Ɛ -SVRM model optimization based on DE algorithm has better prediction capability compared with the methods based on Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).