In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets. In the proposed genetic algorithm/support vector machine (GA-SVM) and genetic algorithm/k nearest neighbor (GA-KNN) hybrid methods, genetic algorithm is improved using Pearson's correlation coefficient, Relief-F, or mutual information. Crossover and selection operations of the genetic algorithm are specialized. Eight different gene expression datasets are used for classification process. The classification performances of the proposed methods are compared with the traditional GA-KNN and GA-SVM wrapper methods and other studies in the literature. Classification results demonstrate that higher accuracy rates are obtained with the proposed methods compared to the other methods for all datasets.
SÖNMEZ, ÖZNUR SİNEM; DAĞTEKİN, MUSTAFA; and ENSARİ, TOLGA
"Gene expression data classification using genetic algorithm-basedfeature selection,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29:
7, Article 17.
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss7/17