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Challenges 2014, 5(2), 450-472; doi:10.3390/challe5020450

An Evolutionary Optimizer of libsvm Models

1
Laboratoire de Chémoinformatique, UMR 7140 CNRS—University Strasbourg, 1 Rue Blaise Pascal, 6700 Strasbourg, France
2
Department of Clinical Systems Onco-Informatics, Graduate School of Medicine, Kyoto University, 606-8501 Kyoto, Japan
*
Author to whom correspondence should be addressed.
Received: 1 September 2014 / Revised: 27 October 2014 / Accepted: 29 October 2014 / Published: 24 November 2014
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Abstract

This user guide describes the rationale behind, and the modus operandi of a Unix script-driven package for evolutionary searching of optimal Support Vector Machine model parameters as computed by the libsvm package, leading to support vector machine models of maximal predictive power and robustness. Unlike common libsvm parameterizing engines, the current distribution includes the key choice of best-suited sets of attributes/descriptors, in addition to the classical libsvm operational parameters (kernel choice, kernel parameters, cost, and so forth), allowing a unified search in an enlarged problem space. It relies on an aggressive, repeated cross-validation scheme to ensure a rigorous assessment of model quality. Primarily designed for chemoinformatics applications, it also supports the inclusion of decoy instances, for which the explained property (bioactivity) is, strictly speaking, unknown but presumably “inactive”, thus additionally testing the robustness of a model to noise. The package was developed with parallel computing in mind, supporting execution on both multi-core workstations as well as compute cluster environments. It can be downloaded from http://infochim.u-strasbg.fr/spip.php?rubrique178. View Full-Text
Keywords: chemoinformatics; QSAR; machine learning; libsvm parameter optimization chemoinformatics; QSAR; machine learning; libsvm parameter optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Horvath, D.; Brown, J.B.; Marcou, G.; Varnek, A. An Evolutionary Optimizer of libsvm Models. Challenges 2014, 5, 450-472.

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