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Methods for Gene Selection and Classification of Microarray Dataset

Methods for Gene Selection and Classification of Microarray Dataset

Mekour Norreddine
Copyright: © 2018 |Pages: 12
ISBN13: 9781522530046|ISBN10: 1522530045|EISBN13: 9781522530053
DOI: 10.4018/978-1-5225-3004-6.ch004
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MLA

Norreddine, Mekour. "Methods for Gene Selection and Classification of Microarray Dataset." Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, IGI Global, 2018, pp. 66-77. https://doi.org/10.4018/978-1-5225-3004-6.ch004

APA

Norreddine, M. (2018). Methods for Gene Selection and Classification of Microarray Dataset. In R. Hamou (Ed.), Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management (pp. 66-77). IGI Global. https://doi.org/10.4018/978-1-5225-3004-6.ch004

Chicago

Norreddine, Mekour. "Methods for Gene Selection and Classification of Microarray Dataset." In Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management, edited by Reda Mohamed Hamou, 66-77. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3004-6.ch004

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Abstract

One of the problems that gene expression data resolved is feature selection. There is an important process for choosing which features are important for prediction; there are two general approaches for feature selection: filter approach and wrapper approach. In this chapter, the authors combine the filter approach with method ranked information gain and wrapper approach with a searching method of the genetic algorithm. The authors evaluate their approach on two data sets of gene expression data: Leukemia, and the Central Nervous System. The classifier Decision tree (C4.5) is used for improving the classification performance.

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