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2020 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Information Systems

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

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

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

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