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

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dc.contributor.authorAbdulsalam, S.O.
dc.contributor.authorAbubakar, A.M.
dc.contributor.authorAjao, J.F.
dc.contributor.authorBabatunde, R.S.
dc.contributor.authorOgundokun, R.O.
dc.contributor.authorNnodim, C.T.
dc.contributor.authorArowolo, M.O.
dc.date.accessioned2025-10-29T12:16:41Z
dc.date.available2025-10-29T12:16:41Z
dc.date.issued2020
dc.description.abstractA 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. Keywords: SVM-RFE; ANOVA; Microarray; SVM; Cancer
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6300
dc.language.isoen
dc.publisherSpringer Nature Switzerland, Lecture Notes in Business Information Processing 402: 480 – 492 - Proceedings of 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020
dc.titlePerformance Evaluation of ANOVA and RFE Algorithms for Classifying Microarray Dataset Using SVM
dc.typeArticle
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