Parallel tuning of support vector machine learning parameters for large and unbalanced data sets

Authors:
Tatjana Eitrich;Bruno Lang
Affiliations:
John von Neumann Institute for Computing, Central Institute for Applied Mathematics, Research Centre Juelich, Germany;Applied Computer Science and Scientific Computing Group, Department of Mathematics, University of Wuppertal, Germany
Venue:
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
Year:
2005

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Abstract

We consider the problem of selecting and tuning learning parameters of support vector machines, especially for the classification of large and unbalanced data sets. We show why and how simple models with few parameters should be refined and propose an automated approach for tuning the increased number of parameters in the extended model. Based on a sensitive quality measure we analyze correlations between the number of parameters, the learning cost and the performance of the trained SVM in classifying independent test data. In addition we study the influence of the quality measure on the classification performance and compare the behavior of serial and asynchronous parallel parameter tuning on an IBM p690 cluster.