Parameters optimization of support vector regression based on immune particle swarm optimization algorithm

Authors:
Yan Wang;Juexin Wang;Wei Du;Chen Zhang;Yu Zhang;Chunguang Zhou
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 6
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Learning using an artificial immune system

Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Support vector machines: hype or hallelujah?

ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Practical selection of SVM parameters and noise estimation for SVM regression

Neural Networks
A tutorial on support vector regression

Statistics and Computing
An overview of statistical learning theory

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel 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 Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters 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 sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.