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2020 | OriginalPaper | Chapter

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

Authors : Sulaiman Olaniyi Abdulsalam, Abubakar Adamu Mohammed, Jumoke Falilat Ajao, Ronke S. Babatunde, Roseline Oluwaseun Ogundokun, Chiebuka T. Nnodim, Micheal Olaolu Arowolo

Published in: Information Systems

Publisher: Springer International Publishing

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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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Title: Performance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM
Authors: Sulaiman Olaniyi Abdulsalam
Abubakar Adamu Mohammed
Jumoke Falilat Ajao
Ronke S. Babatunde
Roseline Oluwaseun Ogundokun
Chiebuka T. Nnodim
Micheal Olaolu Arowolo
Publisher: Springer International Publishing
Book: Information Systems
Print ISBN: 978-3-030-63395-0

Electronic ISBN: 978-3-030-63396-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63396-7_32

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