Skip to main content
SpringerLink
Log in

A Novel Grey Wolf Optimization Based Combined Feature Selection Method

  • Conference paper
  • First Online:
Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

In data mining and machine learning area, features targeting and selection are crucial topics in the real world applications. Unfortunately, massive redundant or unrelated features significantly deteriorate the performance of learning algorithm. This paper presents a novel classification model which combined grey wolf optimizer (GWO) and spectral regression discriminant analysis (SRDA) for selecting the most appropriate features. The GWO algorithm is adopted to iteratively update the currently location of the grey wolf population, while the classification algorithm called SRDA is employed to measure the quality of the selected subset of features. The proposed method is compared with genetic algorithm (GA), Jaya, and three recent proposed Rao algorithms also with SRDA as the classifier over a set of UCI machine learning data repository. The experimental results show that the proposed method achieves the lower classification error rate than that of GA and other corresponding methods generally.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://archive.ics.uci.edu/ml/index.php.

References

  1. Agrafiotis, D.K., Cedeno, W.: Feature selection for structure-activity correlation using binary particle swarms. J. Med. Chem. 45(5), 1098–1107 (2002)

    Article  Google Scholar 

  2. Cai, D., He, X., Han, J.: SRDA: an efficient algorithm for large-scale discriminant analysis. IEEE Trans. Knowl. Data Eng. 20(1), 1–12 (2008)

    Article  Google Scholar 

  3. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)

    Article  Google Scholar 

  4. Gheyas, I.A., Smith, L.S.: Feature subset selection in large dimensionality domains. Pattern Recogn. 43(1), 5–13 (2010)

    Article  Google Scholar 

  5. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  6. Jeong, Y., Shin, K.S., Jeong, M.K.: An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems. J. Oper. Res. Soc. 66(4), 529–538 (2015)

    Article  Google Scholar 

  7. Kashef, S., Nezamabadipour, H.: A new feature selection algorithm based on binary ant colony optimization, pp. 50–54 (2013)

    Google Scholar 

  8. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1), 273–324 (1997)

    Article  Google Scholar 

  9. Liu, H., Motoda, H., Setiono, R., Zhao, Z.: Feature selection: an ever evolving frontier in data mining. In: JMLR: Workshop and Conference Proceedings, vol. 10, pp. 4–13 (2010)

    Google Scholar 

  10. Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)

    Article  Google Scholar 

  11. Marill, T., Green, D.M.: On the effectiveness of receptors in recognition systems. IEEE Trans. Inf. Theory 9(1), 11–17 (1963)

    Article  Google Scholar 

  12. Min, F., Hu, Q., Zhu, W.: Feature selection with test cost constraint. Int. J. Approx. Reason. 55(1), 167–179 (2014)

    Article  MathSciNet  Google Scholar 

  13. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  14. Nazir, M., Majidmirza, A., Alikhan, S.: PSO-GA based optimized feature selection using facial and clothing information for gender classification. J. Appl. Res. Technol. 12(1), 145–152 (2014)

    Article  Google Scholar 

  15. Oduntan, I.O., Toulouse, M., Baumgartner, R., Bowman, C.N., Somorjai, R.L., Crainic, T.G.: A multilevel tabu search algorithm for the feature selection problem in biomedical data. Comput. Math. Appl. 55(5), 1019–1033 (2008)

    Article  MathSciNet  Google Scholar 

  16. Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  17. Rao, R.: Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7(1), 19–34 (2016)

    Google Scholar 

  18. Rao, R.V.: Rao algorithms: three metaphor-less simple algorithms for solving optimization problems. Int. J. Ind. Eng. Comput. 11(1), 107–130 (2020)

    Google Scholar 

  19. Santana, L.E.A., Silva, L., Canuto, A.M.P., Pintro, F., Vale, K.O.: A comparative analysis of genetic algorithm and ant colony optimization to select attributes for an heterogeneous ensemble of classifiers, pp. 1–8 (2010)

    Google Scholar 

  20. Siedlecki, W.W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recogn. Lett. 10(5), 335–347 (1989)

    Article  Google Scholar 

  21. Sun, J., Zhou, M., Ai, W., Li, H.: Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry. Risk Manage. 21, 1–28 (2018)

    Google Scholar 

  22. Vafaie, H., De Jong, K.: Genetic algorithms as a tool for feature selection in machine learning. In: International Conference on Tools with Artificial Intelligence, pp. 200–203 (1992)

    Google Scholar 

  23. Whitney, A.W.: A direct method of nonparametric measurement selection. IEEE Trans. Comput. 20(9), 1100–1103 (1971)

    Article  Google Scholar 

  24. Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection, pp. 1–8 (2012)

    Google Scholar 

  25. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Syst. Man Cybern. 43(6), 1656–1671 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  27. Yan, Z., Yuan, C.: Ant colony optimization for feature selection in face recognition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 221–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25948-0_31

    Chapter  Google Scholar 

Download references

Acknowledgments

Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 16010500300, China NSFC under grants 51607177, 61877065, China Postdoctoral Science Foundation (2018M631005) and Natural Science Foundation of Guangdong Province under grants 2018A030310671.

Author information

Authors and Affiliations

  1. Shanghai University, Shanghai, 200000, China

    Haikuan Wang & Zhaoyan Hu

  2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Zhile Yang & Yuanjun Guo

Authors
  1. Haikuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoyan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhile Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanjun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhile Yang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, H., Hu, Z., Yang, Z., Guo, Y. (2020). A Novel Grey Wolf Optimization Based Combined Feature Selection Method. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_45

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3425-6_45

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation