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.
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Lienemann, K., Plötz, T., Fink, G.A. (2007). On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_5
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DOI: https://doi.org/10.1007/978-3-540-72523-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
Online ISBN: 978-3-540-72523-7
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