Classification of Gene Expression Data Using Feature Selection Based on Type Combination Approach Model With Advanced Feature Selection Technology

Abstract

A key step in addressing the classification issue was the selection of genes for removing redundant and irrelevant genes. The proposed Type Combination Approach –Feature Selection(TCA-FS) model uses the efficient feature selection methods, and the classification accuracy can be enhanced. The three classifiers such as K Nearest Neighbour(KNN), Support Vector Machine(SVM) and Random Forest(RF) are selected for evaluating the opted feature selection methods, and prediction accuracy. The effects of three new approaches for feature selection are Improved Recursive Feature Elimination (IRFE), Revised Maximum Information co-efficient (RMIC), as well as Upgraded Masked Painter (UMP), are analysed. These three proposed techniques are compared with existing techniques and are validated with (i) Stability determination test. (ii) Classification accuracy. (iii) Error rates of three proposed techniques are analysed. Due to the selection of proper threshold on classification, the proposed TCA-FS method provides a higher accuracy compared to the existing system.