Kernel-based learning and feature selection analysis for cancer diagnosis

TL;DR: Experimental results demonstrate that the proposed complete cancer diagnostic process through kernel-based learning and feature selection is efficient and provides a higher classification accuracy rate using a reduced number of genes.

Abstract: Graphical abstractDisplay Omitted HighlightsA novel feature selection approach is proposed based on two steps.First step uses SVM-RFE to prefiltre the gene; we select 60% of relevant genes.Second step uses Binary Dragon Fly algorithm to optimal subset of genes.Objective function is the average of classification rate of three Kernel-based classifiers.The numerical results show the efficacy of the proposed approach. DNA microarray is a very active area of research in the molecular diagnosis of cancer. Microarray data are composed of many thousands of features and from tens to hundreds of instances, which make the analysis and diagnosis of cancer very complex. In this case, gene/feature selection becomes an elemental and essential task in data classification. In this paper, we propose a complete cancer diagnostic process through kernel-based learning and feature selection. First, support vector machines recursive feature elimination (SVM-RFE) is used to prefilter the genes. Second, the SVM-RFE is enhanced by using binary dragonfly (BDF), which is a recently developed metaheuristic that has never been benchmarked in the context of feature selection. The objective function is the average of classification accuracy rate generated by three kernel-based learning methods. We conducted a series of experiments on six microarray datasets often used in the literature. Experiment results demonstrate that this approach is efficient and provides a higher classification accuracy rate using a reduced number of genes.