期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：23
页码：3487-3496
出版社：Journal of Theoretical and Applied
摘要：A significant application of gene expression RNA-Seq 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 Principal Component Analysis (PCA) feature extraction dimension reduction technique, and evaluates the relative performance of classification procedures of Support Vector Machine (SVM) kernel classification techniques, namely SVM-Polynomial kernels and SVM-Gaussian kernels. An accuracy and computational performance metrics of the processes were carried out. A malaria vector dataset for Ribonucleic Acid Sequencing (RNA-Seq) classification was used in the study, and 99.68% accuracy was achieved in the classification output result.
