A DIMENSIONAL REDUCED MODEL FOR THE CLASSIFICATION OF RNA-SEQ ANOPHELES GAMBIAE DATA

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.