title: A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization.
abstract: This paper proposes an approach for gene selection in microarray data. The proposed approach consists of a primary filter approach using Fisher criterion which reduces the initial genes and hence the search space and time complexity. Then, a wrapper approach which is based on cellular learning automata (CLA) optimized with ant colony method (ACO) is used to find the set of features which improve the classification accuracy. CLA is applied due to its capability to learn and model complicated relationships. The selected features from the last phase are evaluated using ROC curve and the most effective while smallest feature subset is determined. The classifiers which are evaluated in the proposed framework are K-nearest neighbor; support vector machine and naïve Bayes. The proposed approach is evaluated on 4 microarray datasets. The evaluations confirm that the proposed approach can find the smallest subset of genes while approaching the maximum accuracy.
authors.affiliation: Department of Computer Engineering, Imam Reza International University, Mashhad, Iran. Electronic address: vafaeeshaarbaf@gmail.com.
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Vafaee Sharbaf
authors.affiliation: Department of Computer Engineering, Imam Reza International University, Mashhad, Iran. Electronic address: sa.mosafer90@yahoo.com.
authors.forename: Sara
authors.initials: S
authors.lastname: Mosafer
authors.affiliation: Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran. Electronic address: moattar@mshdiau.ac.ir.
authors.forename: Mohammad Hossein
authors.initials: MH
authors.lastname: Moattar
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D000069550:Machine Learning; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*; D060388:Support Vector Machine
keywords: Ant colony optimization; Cellular learning automata; Gene selection; K-nearest neighbor; Microarray data; Naïve Bayes