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

  • Yan Wang
  • Juexin Wang
  • Wei Du
  • Chuncai Wang
  • Yanchun Liang
  • Chunguang Zhou
  • Lan Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

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 εC δ 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.

Keywords

Immune algorithm Particle swarm optimization Support vector regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yan Wang
    • 1
  • Juexin Wang
    • 1
  • Wei Du
    • 1
  • Chuncai Wang
    • 2
  • Yanchun Liang
    • 1
  • Chunguang Zhou
    • 1
  • Lan Huang
    • 1
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.School of Computer Science and TechnologyChangchun Universtiy of Science and TechnologyChangchunChina

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