Immune Particle Swarm Optimization for Support Vector Regression on Forest Fire Prediction

Authors:
Yan Wang;Juexin Wang;Wei Du;Chuncai Wang;Yanchun Liang;Chunguang Zhou;Lan Huang
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China 130012;College of Computer Science and Technology, Jilin University, Changchun, China 130012;College of Computer Science and Technology, Jilin University, Changchun, China 130012;School of Computer Science and Technology, Changchun Universtiy of Science and Technology, Changchun, China 130022;College of Computer Science and Technology, Jilin University, Changchun, China 130012;College of Computer Science and Technology, Jilin University, Changchun, China 130012;College of Computer Science and Technology, Jilin University, Changchun, China 130012
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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Abstract

An Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Cross Validation standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters ***C *** of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sincx function with additive noise and forest fires dataset, experimental results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.