Improved Equilibrium Optimizer using Density-based Population and Entropy Operator for Feature Selection

Document Type : Research Article

Authors

Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

As the number of features in practical applications grows, the feature selection process becomes increasingly important. Since the feature selection problem is NP-hard, no exact algorithm can determine the optimal subset over a reasonable period of time. However, traditional feature selection methods are time-consuming and tend to get stuck in local optima. Unlike traditional search techniques, metaheuristics are more effective at exploring and exploiting the search domain because they use several operators. Besides these behaviors of metaheuristics, we present an improved Equilibrium Optimizer algorithm using the density of population and entropy operator, which has proven a good exploration ability to provide a promising candidate solution. After that, a new feature selection model is developed based on the improved Equilibrium Optimizer algorithm. K-nearest neighbors is used as an evaluator for the new solutions. In order to test the performance of the proposed algorithm, simulation experiments are conducted on a set of 14 standard test functions containing both unimodal and multimodal functions. To evaluate the effectiveness of the proposed algorithm, 15 UCI benchmark datasets and five metaheuristics, GA, CS, GSA, RDA, and BBA  are applied. The experimental results revealed the effectiveness of our approach in terms of accuracy performance for the feature selection process.

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References

  1. Faramarzi, M. Heidarinejad, B. Stephens, S. Mirjalili, Equilibrium optimizer: a novel optimization algorithm, Knowledge-Based Systems, vol. 191, 2020, doi: 10.1016/j.knosys.2019.105190
  2. Chen, S. Yuan, D. Lv, Y. Xiang, A novel self-learning feature selection approach based on feature attributions, Expert Systems with Applications, vol. 183, 2021, doi: 10.1016/j.eswa.2021.115219
  3. A. Hashim, E.H. Houssein, K. Hussain, M.S. Mabrouk, W. Al-Atabany, Honey badger algorithm: new metaheuristic algorithm for solving optimization problems, Mathematics and Computers in Simulation, vol. 192, pp. 84-110, 2022, doi: 10.1016/j.matcom.2021.08.013
  4. A. Hashim, K. Hussain, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems, Applied Intelligence, vol. 51, pp. 1531–1551, 2021, doi: 10.1007/s10489-020-01893-z
  5. Abdollahzadeh, F. Soleimanian Gharehchopogh, S. Mirjalili, African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems, Computers & Industrial Engineering, vol. 158, 2021, doi: 10.1016/j.cie.2021.107408
  6. P. Jhansi, P.T. Maruthi, Identifying optimal features using whale optimization technique, 2nd International Conference for Emerging Technology, 2021, doi: 10.1109/INCET51464.2021.9456188
  7. Chandrashekar, F. Sahin, A survey on feature selection methods, Computers & Electrical Engineering, vol. 40, pp. 16-28, 2014, doi: 10.1016/j.compeleceng.2013.11.024
  8. Nagpal, S. Arora, S. Dey, Shreya, Feature selection using gravitational search algorithm for biomedical data, Procedia Computer Science, vol. 115, pp. 258-265, 2017, doi: 10.1016/j.procs.2017.09.133
  9. Emary, W. Yamany, A. E. Hassanien, New approach for feature selection based on rough set and bat algorithm, 9th International Conference on Computer Engineering & Systems (ICCES), pp. 346-353, 2014, doi: 10.1109/ICCES.2014.7030984
  10. M. Elgamal, N.M. Yasin, A.Q.M. Sabri, R. Sihwail, M. Tubishat, H. Jarrah, Improved equilibrium optimization algorithm using elite opposition-based learning and new local search strategy for feature selection in medical datasets, Computation, vol. 9, 2021, doi: 10.3390/computation9060068
  11. Maleki, Y. Zeinali, S.T.A. Niaki, A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection, Expert Systems with Applications, Volume 164, 2021, https://doi.org/10.1016/j.eswa.2020.113981.
  12. L. Huang, C. J. Wang, A GA-based feature selection and parameters optimization for support vector machines, Expert Systems with Applications, Volume 31, Issue 2, 2006, pp. 231-240, https://doi.org/10.1016/j.eswa.2005.09.024.
  13. S. Alotaibi, Ensemble technique with optimal feature selection for saudi stock market prediction: a novel hybrid red deer-grey algorithm, IEEE Access, vol. 9, pp. 64929-64944, 2021, doi: 10.1109/ACCESS.2021.3073507
  14. Tarade, M. Mafarja and A.A. Heidari, H. Faris, I. Aljarah, S. Mirjalili, H. Fujita, An evolutionary gravitational search-based feature selection, Information Sciences, vol. 497, pp. 219-239, 2019, doi: 10.1016/j.ins.2019.05.038
  15. Tikhe, T. Joshi, A. Lahorkar, A. Sane, J. Valadi, Feature selection using equilibrium optimizer, Data Engineering and Intelligent Computing, Springer, pp. 307-315, 2021.
  16. Zahra Asghari Varzaneh, Soodeh Hossein, Sepehr Ebrahimi Mood, Mohammad Masoud Javidi, A new hybrid feature selection based on Improved Equilibrium Optimization, Chemometrics and Intelligent Laboratory Systems, Volume 228, 2022, https://doi.org/10.1016/j.chemolab.2022.104618.
  17. Gao, Y. Zhou, Q. Luo, An efficient binary equilibrium optimizer algorithm for feature selection, IEEE Access, vol. 8, pp. 140936-140963, 2020, doi: 10.1109/ACCESS.2020.3013617
  18. Guha, B. Chatterjee, K.H. Sk, S. Ahmed, T. Bhattacharya, R. Sarkar, Py_FS: a python package for feature selection using meta-heuristic optimization algorithms. In: Springer AISC series of 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR-2021), Kolkata, India, 2021.
  19. Too S. Mirjalili, General learning equilibrium optimizer: a new feature selection method for biological data classification, Applied Artificial Intelligence, 2020, doi: 10.1080/08839514.2020.1861407
  20. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Transactions on Evolutionary Computation, vol. 3, pp. 82-102, 1999
  21. Molga, C. Smutnicki, Test functions for optimization needs, Test Functions for Optimization Needs, 2005

22. A.E. Ezugwu, O.J. Adeleke, A.A. Akinyelu, S.Viriri,  A conceptual comparison of several metaheuristic algorithms on continuous optimisation problems, Neural Computing and Applications, vol. 32, pp. 6207–6251, 2020, doi: 10.1007/s00521-019-04132-w

AUT Journal of Modeling and Simulation
Volume 55, Issue 1
June 2023
Pages 17-38

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