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P System as a Computing Tool for Embedded Feature Selection and Classification Method for Microarray Cancer Data

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Membrane Computing (CMC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12687))

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Abstract

Selection of relevant genes is the crucial task for sample classification in microarray data, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance. Due to the problem of higher risk of overfitting in wrapper methods and sensitivity of the best embedded way to filter out factor that leads to unstable model and significantly different gene subsets, in this paper, we propose a novel model for evaluating and improving techniques for selecting informative genes from microarray data. This model inspired by membrane computing and used the kernel P system (kP) as the variant of the P system to improve the performance of the intelligent algorithm, multi-objective binary particle swarm optimization (MObPSO). The proposed model consists of two main parts. First, kP-MObPSO, which resembles a wrapper type feature selection, and the second part that improves the results of the first part through an embedded feature selection and classification idea based on the kP system. Division, rewriting, and input/output rules are used to make interaction among the genes inside and between the particles. The proposed model applied to the colorectal and breast dataset contains 100 genes with six attributes. The embedded part of the model extracts the marker gene sets indicate more stability and reliability based on ROC measure as well as better error rate in comparison to the wrapper part of the model. In the paper, the lowest error rate by an embedded model is displayed as 0.1111 for breast cancer and 0.0769 for colorectal data.

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Acknowledgements

The efforts of grant for Development of Membrane Computing Software (Universiti Kebangsaan Malaysia (UKM), UKM Grant Code: GGP-2019-023) has been acknowledged, as this support has played a vital role in the successful accomplishment of the research.

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Authors and Affiliations

  1. Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Ravie Chandren Muniyandi & Naeimeh Elkhani

Authors
  1. Ravie Chandren Muniyandi
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  2. Naeimeh Elkhani
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Correspondence to Ravie Chandren Muniyandi .

Editor information

Editors and Affiliations

  1. TU Wien, Vienna, Austria

    Rudolf Freund

  2. Mongolian University of Science and Technology, Ulaanbaatar, Mongolia

    Tseren-Onolt Ishdorj

  3. Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  4. Turku Center for Computer Science, Turku, Finland

    Arto Salomaa

  5. University of Milano-Bicocca, Milan, Italy

    Claudio Zandron

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Muniyandi, R.C., Elkhani, N. (2021). P System as a Computing Tool for Embedded Feature Selection and Classification Method for Microarray Cancer Data. In: Freund, R., Ishdorj, TO., Rozenberg, G., Salomaa, A., Zandron, C. (eds) Membrane Computing. CMC 2020. Lecture Notes in Computer Science(), vol 12687. Springer, Cham. https://doi.org/10.1007/978-3-030-77102-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-77102-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77101-0

  • Online ISBN: 978-3-030-77102-7

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