Intelligent Hybrid Approach for Feature Selection

  • Ahmed M. Anter
  • Ahmad Taher AzarEmail author
  • Khaled M. Fouad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


The issues of multitude of noisy, irrelevant, misleading features, and the capability to tackle inaccurate and inconsistent data in real world topics are the justification to turn into one of the most significant needs for feature selection. This paper proposes an intelligent hybrid approach using Rough Set Theory (RST), Chaos Theory and Binary Grey Wolf Optimization Algorithm (CBGWO) for feature selection problems. Ten different chaotic maps are used to estimate and tune GWO parameters. Experiments are applied on complex medical datasets with various uncertainty features and missing values. The performance of the proposed approach is extensively examined and compared with that of existent feature selection algorithms; such as ant lion optimization (ALO), chaotic ant lion optimization (CALO), bat optimization (BAT), whale optimization algorithm (WOA), chaotic whale optimization algorithm (CWOA), binary crow search algorithm (BCSA), and chaotic binary crow search algorithm (BCCSA) algorithms. The achievement of the proposed approach is analyzed using different evaluation criteria. The overall result indicates that the proposed approach delivers better performance, lower error, higher speed and shorter execution time.


Grey Wolf Optimization Algorithm Bio-inspired Rough set theory Optimization Feature selection Classification 



The study is supported and funded under the auspices of the Benha University, Egypt research project titled “Rough Set Hybridization with Metaheuristic Optimization Techniques for Dimensionality Reduction of Big-Data’’. We would like to show our gratitude to the Benha University, Egypt for funding the research project.


  1. 1.
    Anter, A.M., Hassenian, A.E.: Normalized multiple features fusion based on PCA and multiple classifiers voting in CT liver tumor recognition. In: Advances in Soft Computing and Machine Learning in Image Processing, pp. 113–129. Springer, Cham (2018)Google Scholar
  2. 2.
    Azar, A.T., Hassanien, A.E.: Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft. Comput. 19(4), 1115–1127 (2014). Scholar
  3. 3.
    Elbably, D.L., Fouad, K.M.: A hybrid approach for improving data classification-based on PCA and enhanced ELM. Int. J. Adv. Intell. Paradigms (2018). Forthcoming articlesGoogle Scholar
  4. 4.
    Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing 213, 54–65 (2016)CrossRefGoogle Scholar
  5. 5.
    Fouad, K.: A hybrid approach of missing data imputation for upper gastrointestinal diagnosis. Int. J. Adv. Intell. Paradigms (2018). Forthcoming articlesGoogle Scholar
  6. 6.
    Inbarani, H.H., Banu, P.K.N., Azar, A.T.: Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput. Appl. 25(3–4), 793–806 (2014). Scholar
  7. 7.
    Kohli, M., Arora, S.: Chaotic grey wolf optimization algorithm for constrained optimization problems. J. Comput. Des. Eng. (2017)Google Scholar
  8. 8.
    Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013).
  9. 9.
    Mirjalili, S., Mirjalili, S., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  10. 10.
    Mirjalili, S.: How effective is the Grey Wolf optimizer in training multi-layer perceptrons. Appl. Intell. 43(1), 150–161 (2015)CrossRefGoogle Scholar
  11. 11.
    ElSoud, M.A., Anter, A.M.: Computational intelligence optimization algorithm based on meta-heuristic social-spider: case study on CT liver tumor diagnosis. Comput. Intell. 7(4) (2016)Google Scholar
  12. 12.
    Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning. Inf. Sci. 177(1), 41–73 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Rodrigues, D., Pereira, L.A., Nakamura, R.Y., Costa, K.A., Yang, X.S., Souza, A.N., Papa, J.P.: A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst. Appl. 41(5), 2250–2258 (2014)CrossRefGoogle Scholar
  14. 14.
    Sayed, G.I., Hassanien, A.E., Azar, A.T.: Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl. 1–18 (2017)Google Scholar
  15. 15.
    Strogatz, S.H.: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. CRC Press, Boca Raton (2018)CrossRefGoogle Scholar
  16. 16.
    Hassanien, A.E., Alamry, E.: Swarm Intelligence: Principles, Advances, and Applications. CRC, Taylor & Francis Group, Boca Raton (2015). ISBN 9781498741064 - CAT# K26721Google Scholar
  17. 17.
    Swiniarski, R.W., Skowron, A.: Rough set method in feature selection and recognition. Pattern Recogn. Lett. 24(6), 833–849 (2003)CrossRefGoogle Scholar
  18. 18.
    Hassanien, A.E., Suraj, Z., Slezak, D., Lingras, P.: Rough Computing: Theories, Technologies, and Applications. Idea Group Inc. (2008)Google Scholar
  19. 19.
    Anter, A.M., Hassanien, A.E., ElSoud, M.A., Kim, T.H.: Feature selection approach based on social spider algorithm: case study on abdominal CT liver tumor. In: 2015 Seventh International Conference on Advanced Communication and Networking (ACN), pp. 89–94. IEEE, July 2015Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ahmed M. Anter
    • 1
  • Ahmad Taher Azar
    • 2
    • 3
    Email author
  • Khaled M. Fouad
    • 2
  1. 1.Faculty of Computers and InformationBeni-Suef UniversityBeni SuefEgypt
  2. 2.Faculty of Computer and InformationBenha UniversityBenhaEgypt
  3. 3.School of Engineering and Applied SciencesNile University6th of October City, GizaEgypt

Personalised recommendations