@article{scholars13845,
            year = {2020},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {IEEE Access},
           pages = {106247--106263},
          volume = {8},
            note = {cited By 69},
             doi = {10.1109/ACCESS.2020.3000040},
           title = {Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737786&doi=10.1109\%2fACCESS.2020.3000040&partnerID=40&md5=afb3ba464561276cf31fc5b1003d1f47},
        keywords = {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},
        abstract = {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. {\^A}{\copyright} 2013 IEEE.},
          author = {Al-Tashi, Q. and Abdulkadir, S. J. and Rais, H. M. and Mirjalili, S. and Alhussian, H. and Ragab, M. G. and Alqushaibi, A.},
            issn = {21693536}
}