%0 Journal Article %@ 21693536 %A Al-Tashi, Q. %A Abdulkadir, S.J. %A Rais, H.M. %A Mirjalili, S. %A Alhussian, H. %A Ragab, M.G. %A Alqushaibi, A. %D 2020 %F scholars:13845 %I Institute of Electrical and Electronics Engineers Inc. %J IEEE Access %K Benchmarking; Classification (of information); Dimensionality reduction; Feature extraction; Genetic algorithms; Neural networks; Particle swarm optimization (PSO); Screening; Transfer functions, Classification performance; Continuous optimization problems; Feature selection problem; Multi objective particle swarm optimization; Multi-objective optimization problem; Non dominated sorting genetic algorithm (NSGA II); Sigmoid transfer function; Single objective optimization problems, Multiobjective optimization %P 106247-106263 %R 10.1109/ACCESS.2020.3000040 %T Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification %U https://khub.utp.edu.my/scholars/13845/ %V 8 %X Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost. © 2013 IEEE. %Z cited By 69