Abstract
This paper proposes an enhanced feature selection (FS) approach to improve the classification tasks, taking into account data dimensionality as a significant criterion of the dataset. High dimensionality may cause serious problems in classification that degrade the performance of the classifier. Among these problems: generating complex models (overfitting), increasing the learning time, and including redundant and irrelevant features in the learning model. FS is a data mining technique to minimize the number of dimensions (features) by getting rid of redundant and irrelevant features. Meanwhile, FS tries to maximize the classification performance. As FS is an optimization problem, meta-heuristic optimization algorithms can take place to achieve superior results in solving such problems. This paper proposes the Moth Flame Optimization (MFO) algorithm to tackle the FS problem. A new initialization method called opposition-based is proposed. Furthermore, a new update strategy is proposed to alleviate the local minima. The comparative results find that the proposed approach improves the MFO performance and outperforms other similar approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
Chuang, L.-Y., Chang, H.-W., Chung-Jui, T., Yang, C.-H.: Improved binary PSO for feature selection using gene expression data. Comput. Biol. Chem. 32(1), 29–38 (2008)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 IEEE International Conference on Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108. IEEE (1997)
Khurma, R., Castillo, P., Sharieh, A., Aljarah, I.: Feature selection using binary moth flame optimization with time varying flames strategies. In: Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: ECTA, pp. 17–27. INSTICC, SciTePress (2020)
Khurma, R., Castillo, P., Sharieh, A., Aljarah, I.: New fitness functions in binary Harris hawks optimization for gene selection in microarray datasets. In: Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: ECTA, pp. 139–146. INSTICC, SciTePress (2020)
Abu Khurma, R., Aljarah, I.: A review of multiobjective evolutionary algorithms for data clustering problems. In: Aljarah, I., Faris, H., Mirjalili, S. (eds.) Evolutionary Data Clustering: Algorithms and Applications. AIS, pp. 177–199. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4191-3_8
Khurma, R.A., Aljarah, I., Sharieh, A.: Improved moth flame optimization based on Harris hawks for genesselection. J. Theoret. Appl. Inf. Technol. 98, 3794–3807 (2005)
Khurma, R.B., Aljarah, I., Sharieh, A.: An efficient moth flame optimization algorithm using chaotic maps for feature selection in the medical applications. In: Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, pp. 175–182. INSTICC, SciTePress (2020)
Khurma, R.A., Aljarah, I., Sharieh, A.: Rank based moth flame optimisation for feature selection in the medical application. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)
Khurma, R.A., Aljarah, I., Sharieh, A.: A simultaneous moth flame optimizer feature selection approach based on levy flight and selection operators for medical diagnosis. Arabian J. Sci. Eng. 46(9), 8415–8440 (2021). https://doi.org/10.1007/s13369-021-05478-x
Khurma, R.A., Aljarah, I., Sharieh, A., Mirjalili, S.: EvoloPy-FS: an open-source nature-inspired optimization framework in python for feature selection. In: Mirjalili, S., Faris, H., Aljarah, I. (eds.) Evolutionary Machine Learning Techniques. AIS, pp. 131–173. Springer, Singapore (2020). https://doi.org/10.1007/978-981-32-9990-0_8
Abu Khurmaa, R., Aljarah, I., Sharieh, A.: An intelligent feature selection approach based on moth flame optimization for medical diagnosis. Neural Comput. Appl. 33(12), 7165–7204 (2020). https://doi.org/10.1007/s00521-020-05483-5
Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)
Mirjalili, S., Lewis, A.: S-shaped versus v-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013)
Acknowledgments
This work is supported by the Ministerio español de EconomÃa y Competitividad under project PID2020-115570GB-C22 (DemocratAI::UGR).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Khurma, R.A., Aljarah, I., Castillo, P.A., Sabri, K.E. (2022). An Enhanced Opposition-Based Evolutionary Feature Selection Approach. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-02462-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-02461-0
Online ISBN: 978-3-031-02462-7
eBook Packages: Computer ScienceComputer Science (R0)