Enhanced Quantum Inspired Grey Wolf Optimizer for Feature Selection

Enhanced Quantum Inspired Grey Wolf Optimizer for Feature Selection

Автор: Asmaa M. El-Ashry, Mohammed F. Alrahmawy, Magdi Z. Rashad

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 3 vol.12, 2020 года.

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Grey wolf optimizer (GWO) is a nature inspired optimization algorithm. It can be used to solve both minimization and maximization problems. The binary version of GWO (BGWO) uses binary values for wolves’ positions rather than probabilistic values in the original GWO. Integrating BGWO with quantum inspired operations produce a novel enhanced quantum inspired binary grey wolf algorithm (EQI-BGWO). In this paper we used feature selection as an optimization problem to evaluate the performance of our proposed algorithm EQI-BGWO. Our method was evaluated against BGWO method by comparing the fitness value, number of eliminated features and global optima iteration number. it showed a better accuracy and eliminates higher number of features with good performance. Results show that the average error rate enhanced from 0.09 to 0.06 and from 0.53 to 0.52 and from 0.26 to 0.23 for zoo, Lymphography and diabetes dataset respectively using EQI-BGWO, Where the average number of eliminated features was reduced from 6.6 to 6.7 for zoo dataset and from 7.3 to 7.1 for Lymphography dataset and from 2.9 to 3.2 for diabetes dataset.

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Quantum-inspired algorithms, grey wolf optimization, feature selection

Короткий адрес: https://sciup.org/15017498

IDR: 15017498   |   DOI: 10.5815/ijisa.2020.03.02

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