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Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM

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Information Systems (EMCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 402))

Abstract

A significant application of microarray gene expression data is the classification and prediction of biological models. An essential component of data analysis is dimension reduction. This study presents a comparison study on a reduced data using Analysis of Variance (ANOVA) and Recursive Feature Elimination (RFE) feature selection dimension reduction techniques, and evaluates the relative performance evaluation of classification procedures of Support Vector Machine (SVM) classification technique. In this study, an accuracy and computational performance metrics of the processes were carried out on a microarray colon cancer dataset for classification, SVM-RFE achieved 93% compared to ANOVA with 87% accuracy in the classification output result.

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Authors and Affiliations

  1. Kwara State University, Malete, Nigeria

    Sulaiman Olaniyi Abdulsalam, Abubakar Adamu Mohammed, Jumoke Falilat Ajao, Ronke S. Babatunde & Micheal Olaolu Arowolo

  2. Landmark University, Omu-Aran, Nigeria

    Roseline Oluwaseun Ogundokun & Chiebuka T. Nnodim

Authors
  1. Sulaiman Olaniyi Abdulsalam
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  2. Abubakar Adamu Mohammed
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  3. Jumoke Falilat Ajao
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  4. Ronke S. Babatunde
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  5. Roseline Oluwaseun Ogundokun
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  6. Chiebuka T. Nnodim
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  7. Micheal Olaolu Arowolo
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Corresponding author

Correspondence to Micheal Olaolu Arowolo .

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Editors and Affiliations

  1. Department of Digital Innovation, School of Business, University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  3. School of Strategy and Leadership, Coventry University, Coventry, UK

    Muhammad Mustafa Kamal

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Abdulsalam, S.O. et al. (2020). Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM. In: Themistocleous, M., Papadaki, M., Kamal, M.M. (eds) Information Systems. EMCIS 2020. Lecture Notes in Business Information Processing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-030-63396-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-63396-7_32

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-63396-7

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