Multiclass Feature Selection and Classification with Support Vector Machine in Genomic Study

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dc.contributor.authorBanjoko A. W.
dc.contributor.authorYahya W. B.
dc.contributor.authorGarba M. K.
dc.contributor.authorOlaniran O. R.
dc.contributor.authorAmusa L. B.
dc.contributor.authorGatta N. F.
dc.contributor.authorDauda K. A.
dc.contributor.authorOlorede K. O.
dc.date.accessioned2025-05-27T14:49:00Z
dc.date.available2025-05-27T14:49:00Z
dc.date.issued2017
dc.description.abstractThis study proposes an efficient Support Vector Machine (SVM) algorithm for feature selection and classification of multiclass response group in high dimensional (microarray) data. The Feature selection stage of the algorithm employed the F-statistic of the ANOVA–like testing scheme at some chosen family-wise-error-rate (FWER) to control for the detection of some false positive features. In a 10-fold cross validation, the hyper-parameters of the SVM were tuned to determine the appropriate kernel using one-versus-all approach. The entire simulated dataset was randomly partitioned into 95% training and 5% test sets with the SVM classifier built on the training sets while its prediction accuracy on the response class was assessed on the test sets over 1000 Monte-Carlo cross-validation (MCCV) runs. The classification results of the proposed classifier were assessed using the Misclassification Error Rates (MERs) and other performance indices. Results from the Monte-Carlo study showed that the proposed SVM classifier was quite efficient by yielding high prediction accuracy of the response groups with fewer differentially expressed features than when all the features were employed for classification. The performance of this new method on some published cancer data sets shall be examined vis-à-vis other state-of-the-earth machine learning methods in future works.
dc.identifier.citation3. Banjoko A. W., Yahya W. B., Garba M. K., Olaniran O. R., Amusa L. B., Gatta N. F., Dauda K. A., and Olorede K. O. (2017). Multiclass Feature Selection and Classification with Support Vector Machine in Genomic Study. In Proceedings of the 1st International Conference of the Professional Statisticians Society of Nigeria (PSSN), Vol. (1) 104-109.
dc.identifier.urihttps://www.pssng.org/publication/proceedings
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/5358
dc.language.isoen
dc.publisherProfessional Statisticians Society of Nigeria (PSSN)
dc.relation.ispartofseries1
dc.titleMulticlass Feature Selection and Classification with Support Vector Machine in Genomic Study
dc.title.alternativeIn Proceedings of the 1st International Conference of the Professional Statisticians Society of Nigeria (PSSN)
dc.typeArticle
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