Journal
IEEE ACCESS
Volume 7, Issue -, Pages 39496-39508Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2906757
Keywords
Feature selection; hybrid binary optimization; grey wolf optimization; particle swarm optimization; classification
Categories
Funding
- Centre for Research in Data Science (CERDAS), Universiti Teknologi PETRONAS
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A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.
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