Skip to main content
SpringerLink
Log in

Feature Selection Using Chaotic Salp Swarm Algorithm for Data Classification

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. Despite high performance of SSA, slow convergence speed and getting stuck in local optima are two disadvantages of SSA. This paper introduces a novel chaotic SSA algorithm (CSSA) to avoid these weaknesses, where chaotic maps are used to enhance the performance of SSA algorithm. The CSSA algorithm is incorporated with the K-nearest neighbor classifier to solve the feature selection problem, in which twenty-seven datasets are used to assess the performance of CSSA algorithm. The results confirmed that the proposed chaotic SSA (especially Tent map) produced superior results compared to standard SSA and other optimization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Harvey, D.Y.; Todd, M.D.: Automated feature design for numeric sequence classification by genetic programming. IEEE Trans. Evolut. Comput. 19(4), 474–489 (2015)

    Article  Google Scholar 

  2. Oh, L.; Lee, J.; Moon, B.: Hybrid genetic algorithms for feature selection. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1424–1437 (2004)

    Article  Google Scholar 

  3. Cover, T.M.; Van Campenhout, J.M.: On the possible orderings in the measurement selection problem. IEEE Trans. Syst. Man Cybern. 7(9), 657–661 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  4. Caruccio, L.; Deufemia, V.; Polese, G.: On the discovery of relaxed functional dependencies. In: Proceedings of the 20th International Database Engineering and Applications Symposium, pp. 53–61 (2016)

  5. Deufemia, V.; Caruccio, L.; Polese, G.: Evolutionary mining of relaxed dependencies from big data collections. In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, p. 5 (2017)

  6. Mirjalili, S.; Gandomi, A.H.; Mirjalili, S.Z.; Saremi, S.; Faris, H.; Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)

    Article  Google Scholar 

  7. Fallahi, K.; Raoufi, R.; Khoshbin, H.: An application of Chen system for secure chaotic communication based on extended Kalman filter and multi-shift cipher algorithm. Commun. Nonlinear Sci. Numer. Simul. 13(4), 763–781 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Yang, D.; Li, G.; Cheng, G.: On the efficiency of chaos optimization algorithms for global optimization. Chaos, Solitons Fractals 34(4), 1366–1375 (2007)

    Article  MathSciNet  Google Scholar 

  9. Emary, E.; Zawbaa, H.M.: Impact of chaos functions on modern swarm optimizers. PloS ONE 11(7), e0158738 (2016)

    Article  Google Scholar 

  10. Zhou, Z.; Zhu, S.; Zhang, D.: A novel K-harmonic means clustering based on enhanced Firefly algorithm. In: International Conference on Intelligent Science and Big Data Engineering, vol. 9243, pp. 140–149. Springer (2015)

  11. Gandomi, A.H.; Yang, X.S.; Talatahari, S.; Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  12. Saremi, S.; Mirjalili, S.; Lewis, A.: Biogeography-based optimisation with chaos. Neural Comput. Appl. 25(5), 1077–1097 (2014)

    Article  Google Scholar 

  13. Gandomi, A.H.; Yang, X.S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014)

    Article  MathSciNet  Google Scholar 

  14. Abdullah, A.S.; Ramya, C.; Priyadharsini, V.; Reshma, C.; Selvakumar, S.: A survey on evolutionary techniques for feature selection. In:Conference on Emerging Devices and Smart Systems (ICEDSS), pp. 58–62 (2017)

  15. Xue, B.; Zhang, M.; Browne, W.N.; Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evolut. Comput. 20(4), 606–626 (2016)

    Article  Google Scholar 

  16. Zhang, Y.; Gong, D.; Hu, Y.; Zhang, W.: Feature selection algorithm based on bare bones particle swarm optimization. Neurocomputing 148, 150–157 (2015)

    Article  Google Scholar 

  17. Aziz, M.A.E.; Hassanien, A.E.: Modified cuckoo search algorithm with rough sets for feature selection. Neural Comput. Appl. 29(4), 925–934 (2016)

    Article  Google Scholar 

  18. Hegazy, A.E.; Makhlouf, M.; El-Tawel, G.S.: Dimensionality reduction using an improved whale optimization algorithm for data classification. Int. J. Mod. Educ. Comput. Sci. 10(7), 37 (2018)

    Article  Google Scholar 

  19. Emary, E.; Zawbaa, H.M.; Hassanien, A.E.: Binary ant lion approaches for feature selection. Neurocomputing 213, 54–65 (2016)

    Article  Google Scholar 

  20. Wang, G.; Guo, L.; Gandomi, A.H.; Hao, G.; Wang, H.: Chaotic Krill Herd algorithm. Inf. Sci. 274, 17–34 (2014)

    Article  MathSciNet  Google Scholar 

  21. Mitić, M.; Vuković, N.; Petrović, M.; Miljković, Z.: Chaotic fruit fly optimization algorithm. Knowl Based Syst 89, 446–458 (2015)

    Article  Google Scholar 

  22. Zawbaa, H.M.; Emary, E.; Grosan, C.: Feature selection via chaotic Antlion optimization. PLOS ONE 11(3), e0150652 (2016)

    Article  Google Scholar 

  23. Chuang, L.Y.; Yang, C.H.; Li, J.C.: Chaotic maps based on binary particle swarm optimization for feature selection. Appl. Soft Comput. 11(1), 239–248 (2011)

    Article  Google Scholar 

  24. Hegazy, A.E.; Makhlouf, M.; El-Tawel, G.S.: Improved salp swarm algorithm for feature selection. J. King Saud Univ. Comput. Inf. Sci. (2018). https://doi.org/10.1016/j.jksuci.2018.06.003

  25. Li, M.; Du, W.; Yuan, L.: Feature selection of face recognition based on improved chaos genetic algorithm. In: 2010 Third international symposium on electronic commerce and security, pp. 74–78. IEEE (2010)

  26. Ewees, A.A.; Aziz, M.A.E.; Hassanien, A.E.: Chaotic multi-verse optimizer-based feature selection. Neural Comput. Appl. (2017). https://doi.org/10.1007/s00521-017-3131-4

  27. Emary, E.; Zawbaa, H.M.; Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371–381 (2016)

    Article  Google Scholar 

  28. Chuang, L.Y.; Chang, H.W.; Tu, C.J.; Yang, C.H.: Improved binary PSO for feature selection using gene expression data. Comput. Biol. Chem. 32(1), 29–38 (2008)

    Article  MATH  Google Scholar 

  29. “UCI Machine Learning Repository: Data Sets.” [Online]. Available: http://archive.ics.uci.edu/ml/index.php. Accessed 14 Feb 2018

  30. Hastie, T.; Friedman, J.; Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer, Berlin (2001)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt

    Ah. E. Hegazy, M. A. Makhlouf & Gh. S. El-Tawel

  2. Faculty of Computers and Informatics, Nahda University, Beni Suef, Egypt

    M. A. Makhlouf

Authors
  1. Ah. E. Hegazy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Makhlouf
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gh. S. El-Tawel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ah. E. Hegazy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hegazy, A.E., Makhlouf, M.A. & El-Tawel, G.S. Feature Selection Using Chaotic Salp Swarm Algorithm for Data Classification. Arab J Sci Eng 44, 3801–3816 (2019). https://doi.org/10.1007/s13369-018-3680-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-018-3680-6

Keywords

Search

Navigation