Abstract
We present a novel approach to gene selection for microarry data through the sensitivity analysis of support vector machines (SVMs). A new measurement (sensitivity) is defined to quantify the saliencies of individual features (genes) by analyzing the discriminative function in SVMs. Our feature selection strategy is first to select the features with higher sensitivities but meanwhile keep the remaining ones, and then refine the selected subset by tentatively substituting some part with fragments of the previously rejected features. The accuracy of our method is validated experimentally on the benchmark microarray datasets.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, D., Yeung, D.S., Tsang, E.C.C., Shi, L. (2005). Gene Selection Through Sensitivity Analysis of Support Vector Machines. In: R. Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds) Computational Life Sciences. CompLife 2005. Lecture Notes in Computer Science(), vol 3695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11560500_11
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DOI: https://doi.org/10.1007/11560500_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29104-6
Online ISBN: 978-3-540-31726-5
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