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Michael B. Fenn, Vijay Pappu, Pando G. Georgeiv and Panos M. Pardalos Raman spectroscopy utilizing Fisher-based feature selection combined with Support Vector Machines for the characterization of breast cell lines Journal of Raman Spectroscopy 44

Article first published online: 22 MAY 2013 | DOI: 10.1002/jrs.4309

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We have developed a novel data mining framework optimized for Raman datasets called Fisher-based Feature Selection-Support Vector Machines (FFS-SVM). FFS-SVM provides simultaneous supervised classification and user-defined Fisher criterion-based feature selection, reducing overfitting and directly yielding significant wavenumbers. We investigate five breast cell lines using Raman microspectroscopy and by applying the FFS-SVM framework, we achieve both high classification accuracy and extraction of biologically significant features thus, providing comprehensive Raman based cell-line characterization and analysis.

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