DNA Gene Expression Analysis on Diffuse Large B-Cell Lymphoma (DLBCL) Based on Filter Selection Method with Supervised Classification Method

Abstract

The exponential growth of DNA dataset in the scientific repository has been encouraging interdisciplinary research on ecology, computer science, and bioinformatics. For better classification of cancer (DNA gene expression), many technologies are useful as demonstrated by a prior experimental study. The major challenging task of gene selection method is extracting informative genes contribution in the classification from the DNA microarray datasets at low computational cost. In this paper, amalgamation of Spearman’s correlation (SC) and filter-based feature selection (FS) methods is proposed. We demonstrate the extensive comparison of the effect of Spearman’s correlation with FS methods, i.e., Relief-F, Joint Mutual Information (JMI), and max-relevance and min-redundancy (MRMR). To measure the classification performance, four diverse supervised classifiers, i.e., K-nearest neighbor (K-NN), support vector machines (SVM), naive Bayes (NB), and decision tree (DT), have been used on DLBCL dataset. The result demonstrates that Spearman’s correlation in conglomeration with MRMR performs better than other combinations.