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A Meta-Review of Feature Selection Techniques in the Context of Microarray Data

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Bioinformatics and Biomedical Engineering (IWBBIO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10208))

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Abstract

Microarray technologies produce very large amounts of data that need to be classified for interpretation. Large data coupled with small sample sizes make it challenging for researchers to get useful information and therefore a lot of effort goes into the design and testing of feature selection tools; literature abounds with description of numerous methods. In this paper we select five representative review papers in the field of feature selection for microarray data in order to understand their underlying classification of methods. Finally, on this base, we propose an extended taxonomy for categorizing feature selection techniques and use it to classify the main methods presented in the selected reviews.

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Authors and Affiliations

  1. University of Mauritius, Reduit, Mauritius

    Zahra Mungloo-Dilmohamud & Yasmina Jaufeerally-Fakim

  2. University of Applied Sciences Western Switzerland (HES-SO), School of Business and Engineering Vaud (HEIG-VD), Swiss Institute of Bioinformatics (SIB), CI4CB, Computational Intelligence for Computational Biology Group, Yverdon, Switzerland

    Carlos Peña-Reyes

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  1. Zahra Mungloo-Dilmohamud
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  2. Yasmina Jaufeerally-Fakim
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  3. Carlos Peña-Reyes
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Correspondence to Zahra Mungloo-Dilmohamud .

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  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. Universidad de Granada, Granada, Spain

    Francisco Ortuño

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Mungloo-Dilmohamud, Z., Jaufeerally-Fakim, Y., Peña-Reyes, C. (2017). A Meta-Review of Feature Selection Techniques in the Context of Microarray Data. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-56148-6_3

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