On the application of SVM-ensembles based on adapted random subspace sampling for automatic classification of NMR data

Authors:
Kai Lienemann;Thomas Plötz;Gernot A. Fink
Affiliations:
University of Dortmund, Intelligent Systems Group, Germany;University of Dortmund, Intelligent Systems Group, Germany;University of Dortmund, Intelligent Systems Group, Germany
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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Abstract

We present an approach for the automatic classification of Nuclear Magnetic Resonance Spectroscopy data of biofluids with respect to drug induced organ toxicities. Classification is realized by an Ensemble of Support Vector Machines, trained on different subspaces according to a modified version of Random Subspace Sampling. Features most likely leading to an improved classification accuracy are favored by the determination of subspaces, resulting in an improved classification accuracy of base classifiers within the Ensemble. An experimental evaluation based on a challenging, real task from pharmacology proves the increased classification accuracy of the proposed Ensemble creation approach compared to single SVM classification and classical Random Subspace Sampling.