Efficient Support Vector Machine Classification of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma mRNA Tissue Samples

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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.authorDauda, K. A.
dc.contributor.authorOlorede, K. O.
dc.date.accessioned2024-04-17T08:25:24Z
dc.date.available2024-04-17T08:25:24Z
dc.date.issued2015-04-25
dc.description.abstractThis paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.en_US
dc.description.sponsorshipSelfen_US
dc.identifier.urihttp://anale-informatica.tibiscus.ro/download/lucrari/13-2-08-Banjoko.pdf
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/11920
dc.publisherAnnals Computer Science Seriesen_US
dc.relation.ispartofseries13;2
dc.subjectSVMen_US
dc.subjectDiffuse Large B-Cell Lymphomaen_US
dc.subjectFollicular Lymphomaen_US
dc.subject10-fold cross-validationen_US
dc.subjectWelch Statisticen_US
dc.titleEfficient Support Vector Machine Classification of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma mRNA Tissue Samplesen_US
dc.typeArticleen_US

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