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A Fusion-Based Feature Selection Framework for Microarray Data Classification

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

Abstract

Gene expression profiling uses microarray techniques to discover patterns of genes when they are expressed. This helps to draw a picture of how the cell performs its function and determines whether there are any mutations. However, microarrays generate a huge amount of data which causes a computational cost and is time-consuming in the analysis process. Feature selection is one of the solutions for reducing the dimensionality of microarray datasets by choosing important genes and eliminating redundant and irrelevant features. In this study, a fusion-based feature selection framework was proposed that aims to apply multiple feature selection methods and combine them using ensemble methods. The framework consists of three layers; in the first layer, there are three feature selection methods that worked independently for ranking genes and assigned a score for each gene. In the second layer, a threshold is used to filter each gene according to their calculated scores. In the last layer, the final decision about which genes are important is made based on one of the decision voting strategies, either majority or consensus. The proposed framework presented an improvement in terms of classification accuracy and dimensionality reduction when compared with other previous methods.

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Author information

Authors and Affiliations

  1. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Talal Almutiri

  2. Department of Information Systems, College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed & Essa Abdullah Hezzam

  3. Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia

    Manar Alassaf

Authors
  1. Talal Almutiri
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  2. Faisal Saeed
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  3. Manar Alassaf
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  4. Essa Abdullah Hezzam
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Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Information Systems Department, Universiti Utara Malaysia, Sintok, Malaysia

    Fathey Mohammed

  3. Sanaa’a Community College, Sana'a, Yemen

    Abdulaziz Al-Nahari

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Almutiri, T., Saeed, F., Alassaf, M., Hezzam, E.A. (2021). A Fusion-Based Feature Selection Framework for Microarray Data Classification. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_52

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