Evolving Kernel Functions for SVMs by Genetic Programming

Authors:
Laura Diosan;Alexandrina Rogozan;Jean Pierre Pecuchet
Affiliations:
-;-;-
Venue:
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Year:
2007

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Applied Intelligence

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Abstract

A hybrid model for evolving Support Vector Machine (SVM) kernel functions is developed in this paper. The kernel expression is considered as a parameter of the SVM algorithm and the current approach tries to find the best expression for this SVM parameter. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Support Vector Machine (SVM) algorithm. Each GP chromosome is a tree encoding the mathematical expression for the kernel function. The evolved kernel is compared to several human-designed kernels and to a previous genetic kernel on several datasets. Numerical experiments show that the SVM embedding our evolved kernel performs statistically better than standard kernels, but also than previous genetic kernel for all considered classification problems.