Uncertainty Handling in Model Selection for Support Vector Machines

Authors:
Tobias Glasmachers;Christian Igel
Affiliations:
Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany;Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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Abstract

We consider evolutionary model selection for support vector machines. Hold-out set-based objective functions are natural model selection criteria, and we introduce a symmetrization of the standard cross-validation approach. We propose the covariance matrix adaptation evolution strategy (CMA-ES) with uncertainty handling for optimizing the new randomized objective function. Our results show that this search strategy avoids premature convergence and results in improved classification accuracy compared to strategies without uncertainty handling.