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Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection

Christian Gagné, Marc Schoenauer, Michèle Sebag and Marco Tomassini

Abstract - Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) non-linear learning can be brought into linear learning thanksto the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin criterion, and a co-evolution framework ensuring the computational scalability of the approach.Empirical validation on standard ML benchmark demonstrates that EKM is competitive using state-of-the-art SVMs with tuned hyper-parameters.

Bibtex:

@inproceedings{Gagné639,
author    = { Christian Gagné and Marc Schoenauer and Michèle Sebag and Marco Tomassini },
title     = { Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection },
booktitle = { Parallel Problem Solving from Nature (PPSN IX) },
year      = { 2006 },
month     = { September 9-13 },
location  = { Reykjavik, Iceland }
}

Last modification: 2006/06/16 by cgagne

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