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

Список литературы Enhanced Quantum Inspired Grey Wolf Optimizer for Feature Selection

  • Miao, Jianyu, and Lingfeng Niu. "A survey on feature selection." Procedia Computer Science 91 (2016): 919-926.
  • Jović, Alan, Karla Brkić, and Nikola Bogunović. "A review of feature selection methods with applications." 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, 2015.
  • Chandrashekar G, Sahin F. A survey on feature selection methods. Computers & Electrical Engineering. 2014 Jan 1;40(1):16-28.
  • Bolón-Canedo, Verónica, Noelia Sánchez-Maroño, and Amparo Alonso-Betanzos. Feature selection for high-dimensional data. Cham: Springer, 2015.
  • Guyon, I., Weston, J., Barnhill, S. and Vapnik, V. Gene selection for cancer classification using support vector machines. Machine Learning, 46(13):389–422, 2002.
  • Mej´ıa-Lavalle, M., Sucar, E. and Arroyo, G. Feature selection with a perceptron neural net. In International Workshop on Feature Selection for Data Mining, pages 131–135, 2006
  • Josiński, Henryk, et al. "Heuristic method of feature selection for person re-identification based on gait motion capture data." Asian Conference on Intelligent Information and Database Systems. Springer, Cham, 2014.
  • Chen, Hao, et al. "A heuristic feature selection approach for text categorization by using chaos optimization and genetic algorithm."Mathematical problems in Engineering 2013 (2013).
  • Too, Jingwei, et al. "A new competitive binary Grey Wolf Optimizer to solve the feature selection problem in EMG signals classification." Computers 7.4 (2018): 58.‏ ;-
  • Abdel-Basset, Mohamed, et al. "A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection." Expert Systems with Applications 139 (2020): 112824.‏
  • Emary, Eid, Hossam M. Zawbaa, and Aboul Ella Hassanien. "Binary grey wolf optimization approaches for feature selection." Neurocomputing 172 (2016): 371-381.
  • Barani, Fatemeh, Mina Mirhosseini, and Hossein Nezamabadi-Pour. "Application of binary quantum-inspired gravitational search algorithm in feature subset selection." Applied Intelligence 47.2 (2017): 304-318.‏
  • Zawbaa, Hossam M., et al. "Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach." Swarm and Evolutionary Computation 42 (2018): 29-42.
  • Mirjalili, Seyedali. Evolutionary Machine Learning Techniques: Algorithms and Applications. Springer Nature, 2020.
  • Mirjalili, Seyedali, Seyed Mohammad Mirjalili, and Andrew Lewis. "Grey wolf optimizer." Advances in engineering software 69 (2014): 46-61.
  • Srikanth, K., et al. "Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem." Computers & Electrical Engineering 70 (2018): 243-260.
  • Grover, Lov K. "Quantum mechanics helps in searching for a needle in a haystack." Physical review letters 79.2 (1997): 325.
  • Shor, Peter W. "Algorithms for quantum computation: Discrete logarithms and factoring." Proceedings 35th annual symposium on foundations of computer science. Ieee, 1994.
  • McMahon, David. Quantum computing explained. John Wiley & Sons, 2007.
  • Tang, Ewin. "A quantum-inspired classical algorithm for recommendation systems." Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. ACM, 2019.
  • Han, Kuk-Hyun, and Jong-Hwan Kim. "Quantum-inspired evolutionary algorithm for a class of combinatorial optimization." IEEE transactions on evolutionary computation 6.6 (2002): 580-593.
  • Layeb, Abdesslem. "A novel quantum inspired cuckoo search for knapsack problems." International Journal of bio-inspired Computation 3.5 (2011): 297-305.
  • Zouache, Djaafar, Farid Nouioua, and Abdelouahab Moussaoui. "Quantum-inspired firefly algorithm with particle swarm optimization for discrete optimization problems." Soft Computing 20.7 (2016): 2781-2799.
  • Darwish, Ashraf. "Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications." Future Computing and Informatics Journal 3.2 (2018): 231-246.
  • Raymer, Michael L., et al. "Dimensionality reduction using genetic algorithms." IEEE transactions on evolutionary computation 4.2 (2000): 164-171.
  • Xiong, Hegen, et al. "Quantum rotation gate in quantum-inspired evolutionary algorithm: A review, analysis and comparison study." Swarm and Evolutionary Computation 42 (2018): 43-57.
  • Yuan, Xiaohui, et al. "A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem." Energy conversion and management 100 (2015): 1-9.
  • Abdull Hamed, H. N., N. Kasabov, and Siti Mariyam Shamsuddin. Quantum-inspired particle swarm optimization for feature selection and parameter optimization in evolving spiking neural networks for classification tasks. InTech, 2011.‏
  • Probabilistic Evolving Spiking Neural Network Optimization Using Dynamic Quantum-inspired Particle Swarm Optimization
  • Cunningham, Padraig, and Sarah Jane Delany."k-Nearest neighbour classifiers." Multiple Classifier Systems 34.8 (2007): 1-17
  • C. T. Bhunia. Introduction to quantum computing. 2010.
  • Mirjalili, Seyedali, and Andrew Lewis. "The whale optimization algorithm. "Advances in engineering software 95 (2016): 51-67.
  • Uymaz, Sait Ali, Gulay Tezel, and Esra Yel. "Artificial algae algorithm (AAA) for nonlinear global optimization.” Applied Soft Computing 31 (2015): 153-171.
  • Xiao Lei LI,Zhi Jiang SHAO,Ji Xin QIAN. An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm [J]. Systems Engineering - Theory & Practice, 2002, 22(11): 32-38.
  • Tawhid, Mohamed A., and Kevin B. Dsouza. "Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems." Applied Computing and Informatics (2018).‏
  • Ramos, Alimed Celecia, and Marley Vellasco. "Quantum-inspired Evolutionary Algorithm for Feature Selection in Motor Imagery EEG Classification." 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018.‏
  • A. Frank, A. Asuncion, UCI Machine Learning Repository, 2010.
  • Emary, Eid, Hossam M. Zawbaa, and Aboul Ella Hassanien. "Binary grey wolf optimization approaches for feature selection." Neurocomputing 172 (2016): 371-381.
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