Particle swarm optimization for parameter determination and feature selection of support vector machines

@article{Lin2008ParticleSO,
  title={Particle swarm optimization for parameter determination and feature selection of support vector machines},
  author={Shih-Wei Lin and Kuo-Ching Ying and Shih-Chieh Chen and Zne-Jung Lee},
  journal={Expert Syst. Appl.},
  year={2008},
  volume={35},
  pages={1817-1824}
}
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without reducing SVM classification accuracy. A particle swarm optimization (PSO) based approach for parameter determination and feature selection of the… CONTINUE READING
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