Identification of cancerous gene groups from microarray data by employing adaptive genetic and support vector machine technique. (30th October 2019)

• Record Type: Journal Article
• Authors: Shukla, Alok Kumar
• Is Part Of: Computational intelligence. Volume 36:Number 1(2020)
• Page Start: 102
• Page End: 131
• Publication Date: 2019-10-30
• Subjects: adaptive genetic algorithm -- classification -- multipopulation -- naïve Bayes -- gene selection -- support vector machine; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12245
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Nowadays, microarray gene expression data plays a vital role in tumor classification. However, due to the accessibility of a limited number of tissues compared to large number of genes in genomic data, various existing methods have failed to identify a small subset of discriminative genes. To overcome this limitation, in this paper, we developed a new hybrid technique for gene selection, called ensemble multipopulation adaptive genetic algorithm (EMPAGA) that can overlook the irrelevant genes and classify cancer accurately. The proposed hybrid gene selection algorithm comprises of two phase. In the first phase, an ensemble gene selection (EGS) method used to filter the noisy and redundant genes in high‐dimensional datasets by combining multilayer and F ‐score approaches. Then, an adaptive genetic algorithm based on multipopulation strategy with support vector machine and naïve Bayes (NB) classifiers as a fitness function is applied for gene selection to select the extremely sensible genes from the reduced datasets. The performance of the proposed method is estimated on 10 microarray datasets of numerous tumor. The comprehensive results and various comparisons disclose that EGS has a remarkable impact on the efficacy of the adaptive genetic algorithm with multipopulation strategy and enhance the capability of the proposed approach in terms of convergence rate and solution quality. The experiments results demonstrate the superiority of the proposed method when compared to other standard wrappers regarding classification accuracy and optimal number of genes.