Bias and Variance Multi-objective Optimization for Support Vector Machines Model Selection

  • Alejandro Rosales-Pérez
  • Hugo Jair Escalante
  • Jesus A. Gonzalez
  • Carlos A. Reyes-Garcia
  • Carlos A. Coello Coello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7887)

Abstract

In this paper, we describe a novel model selection approach for a SVM. Each model can be composed by a feature selection method and a pre-processing method besides the classifier. Our approach is based on a multi-objective evolutionary algorithm and on the bias-variance definition. This strategy allows us to explore the hyperparameters space and to select the solutions with the best bias-variance trade-off. The proposed method is evaluated using a number of benchmark data sets for classification tasks. Experimental results show that it is possible to obtain models with an acceptable generalization performance using the proposed approach.

Keywords

Support vector machines Model selection Bias-variance trade-off Multi-objective optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alejandro Rosales-Pérez
    • 1
  • Hugo Jair Escalante
    • 1
  • Jesus A. Gonzalez
    • 1
  • Carlos A. Reyes-Garcia
    • 1
  • Carlos A. Coello Coello
    • 2
  1. 1.Computer Science DepartmentInstituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)TonantzintlaMexico
  2. 2.Computer Science DepartmentCINVESTAV-IPNMexico CityMexico

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