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Feature Selection in Gene Expression Profile Employing Relevancy and Redundancy Measures and Binary Whale Optimization Algorithm (BWOA)

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13087))

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Abstract

The presence of a large number of genes in the gene expression profiles imposes a computational challenge for cancer classification. To deal with the high-dimensional feature space, in this paper, we present a 3-step feature selection framework, RRO (Relevancy-Redundancy-Optimization). In the first step, RRO identifies top-ranked class-relevant genes utilizing the analysis of variance (ANOVA) and F-test. In the second step, class correlated but redundant genes are removed by employing the Kendall rank correlation coefficient (Kendall’s \(\tau \)). Finally, we utilize a metaheuristic optimization algorithm, binary whale optimization algorithm (BWOA), with the support vector machine (SVM) classifier to select an optimal gene subset. The comparisons with thirteen state-of-the-art methods in ten gene expression datasets show that RRO yields better or comparable accuracy.

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Notes

  1. 1.

    https://github.com/kivancguckiran/microarray-data.

  2. 2.

    http://csse.szu.edu.cn/staff/zhuzx/Datasets.html.

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Authors and Affiliations

  1. Old Dominion University, Norfolk, VA, 23529, USA

    Salim Sazzed

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  1. Salim Sazzed
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Correspondence to Salim Sazzed .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

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Sazzed, S. (2022). Feature Selection in Gene Expression Profile Employing Relevancy and Redundancy Measures and Binary Whale Optimization Algorithm (BWOA). In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-95405-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95404-8

  • Online ISBN: 978-3-030-95405-5

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