Abstract
Recursive feature elimination based on non-linear kernel support vector machine (SVM-RFE) with parameter selection by genetic algorithm is an effective algorithm to perform gene selection and cancer classification in some degree, but its calculating complexity is too high for implementation. In this paper, we propose a new strategy to use adaptive kernel parameters in the recursive feature elimination algorithm implemented with Gaussian kernel SVMs as a better alternatives to the aforementioned algorithm for pragmatic reasons. The proposed method performs well in selecting genes and achieves high classification accuracies with these genes on two cancer datasets.
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© 2006 Springer-Verlag Berlin Heidelberg
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Mao, Y., Zhou, X., Yin, Z., Pi, D., Sun, Y., Wong, S.T.C. (2006). Gene Selection Using Gaussian Kernel Support Vector Machine Based Recursive Feature Elimination with Adaptive Kernel Width Strategy. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_116
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DOI: https://doi.org/10.1007/11795131_116
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)