A Hybrid Genetic Programming and Boosting Technique for Learning Kernel Functions from Training Data

  • Authors:

  • Marta Girdea;Liviu Ciortuz

  • Affiliations:

  • -;-

  • Venue:
  • SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
  • Year:
  • 2007

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Abstract

This paper proposes a technique for learning kernel functions that can be used in non-linear SVM classification. The technique uses genetic programming to evolve kernel functions as additive or multiplicative combinations of linear, polynomial and RBF kernels, while a procedure inspired from InfoBoost helps the evolved kernels concentrate on the most difficult objects to classify. The kernels obtained at each boosting round participate in the training of non-linear SVMs which are combined, along with their confidence coefficients, into a final classifier. We compared on several data sets the performance of the kernels obtained in this manner with the performance of classic RBF kernels and of kernels evolved using a pure GP method, and we concluded that the boosted GP kernels are generally better.