A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata. (2017) - Motieghader, Habib; Najafi, Ali; Sadeghi, Balal; Masoudi-Nejad, Ali
Abstract: Cancer classification is an important problem in cancer diagnosis and treatment. One of the most effective methods in cancer classification is gene selection. However, selecting a subset of genes which increases the classification accuracy is an NP-Hard problem. A variety of algorithms were proposed for gene selection in cancer classification in previous studies. In this study, a hybrid meta-heuristic algorithm, which is an integration of Genetic Algorithm and Learning Automata (GALA), is proposed for this purpose. The time complexity of GALA is O ( G . m . n 3 ) and it has acceptable accuracy and performance on some well-known cancer datasets. To evaluate the performance of GALA, six different cancer datasets including Colon, ALL_AML, SRBCT, MLL, Tumors_9 and Tumors_11 were selected. Based on the evaluation process, the GALA algorithm provided remarkable results on each dataset compared to some recently proposed algorithms. Highlights: Cancer classification is an important problem in cancer diagnosis and treatment. In this paper, a hybrid meta-heuristic algorithm, called GALA, is proposed for cancer classification. GALA algorithm uses both genetic algorithm and learning automata advantages. Six cancer microarray datasets (Colon, ALL_AML, SRBCT, MLL, Tumors_9 and Tumors_11) used for evaluation of GALA algorithm.