Abstract
It is an important subject to find feature genes from microarray expression profiles in the study of microarray technology. In this paper, a hybrid algorithm using SVM and GA is proposed. We first find a feature gene subset and filter most genes which are unrelated with diseases according to certain significant level, gene importance and classification efficiency by Least Square Support Vector Machine. Then we apply an improved genetic algorithm to carry out feature selection, in which the information entropy is used as a fitness function. At last, we apply the proposed feature selection algorithm to the two expression data sets of microarray, evaluate the feature gene subsets that are obtained in different conditions. Simulated results show that both good classification efficiency and the important genes which are related with diseases could be obtained by using the hybrid algorithm.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xiong, W., Zhang, C., Zhou, C., Liang, Y. (2006). Selection for Feature Gene Subset in Microarray Expression Profiles Based on a Hybrid Algorithm Using SVM and GA. In: Min, G., Di Martino, B., Yang, L.T., Guo, M., Rünger, G. (eds) Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops. ISPA 2006. Lecture Notes in Computer Science, vol 4331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11942634_66
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DOI: https://doi.org/10.1007/11942634_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49860-5
Online ISBN: 978-3-540-49862-9
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