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A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data

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Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

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Abstract

We propose a Genetic Algorithm (GA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The GA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. Using archive records of ”good” gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.

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Huerta, E.B., Duval, B., Hao, JK. (2006). A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_4

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  • DOI: https://doi.org/10.1007/11732242_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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