Skip to main content
SpringerLink
Log in

Developing an Efficient Method for Automatic Threshold Detection Based on Hybrid Feature Selection Approach

  • Conference paper
  • First Online:
Artificial Intelligence and Bioinspired Computational Methods (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1225))

Included in the following conference series:

Abstract

Dimensionality reduction is an interesting area of research in data mining. An effective way to reduce dimensions is feature selection that removes irrelevant information meanwhile helping to understand the learning model better and improving prediction accuracy. In this paper, we face a challenge of filter methods to determine number of significant features that achieves better performance since filters don’t evaluate performance based on accuracy but use certain criteria to rank features based on some scores. To handle this challenge, we proposed an effective hybrid approach for feature selection that is a filter-based method inspired by concepts of chi-square, Relief-F, and mutual information. It provides a score for each feature then specifies threshold value automatically based on dataset in use to select important subset of features used to build model, which reduces required execution time and amount of memory. Our proposed approach was analyzed empirically and theoretically to demonstrate its efficiency.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

References

  1. Tang, J., Alelyani, S., Liu, H.: Feature selection for classification: a review. Data Classif. Algorithms Appl. 37‏ (2014)

    Google Scholar 

  2. Jain, D., Singh, V.: An efficient hybrid feature selection model for dimensionality reduction. Proc. Comput. Sci. 132, 333–341 (2018)

    Article  Google Scholar 

  3. 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 

  4. Miao, J., Niu, L.: A survey on feature selection. Proc. Comput. Sci. 91, 919–926 (2016)

    Article  Google Scholar 

  5. Nguyen, H.B., Xue, B., Liu, I., Zhang, M.: Filter based backward elimination in wrapper based PSO for feature selection in classification. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3111–3118. IEEE (2014)

    Google Scholar 

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

    MATH  Google Scholar 

  7. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)

    Article  Google Scholar 

  8. Yu, L., Liu, H.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th International Conference on Machine Learning (ICML), pp. 856–863 (2003)

    Google Scholar 

  9. Kumar, V., Minz, S.: Feature Selection. SmartCR 4(3), 211–229 (2014)

    Article  Google Scholar 

  10. Qin, C.J., Guan, Q., Wang, X.P.: Application of ensemble algorithm integrating multiple criteria feature selection in coronary heart disease detection. Biomed. Eng. Appl. Basis Commun. 29(06), 1750043 (2017)

    Article  Google Scholar 

  11. Haq, A.U., Li, J.P., Memon, M.H., Nazir, S., Sun, R.: A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mob. Inf. Syst. (2018)

    Google Scholar 

  12. Steuer, R., Kurths, J., Daub, C.O., Weise, J., Selbig, J.: The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18(2), S231–S240 (2002)

    Article  Google Scholar 

  13. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 Science and Information Conference, pp. 372–378. IEEE (2014)

    Google Scholar 

  14. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning, pp. 171–182. Springer, Heidelberg (1994)

    Google Scholar 

  15. Sulaiman, M.A., Labadin, J.: Feature selection based on mutual information. In: 2015 9th International Conference on IT in Asia (CITA), pp. 1–6. IEEE (2015)‏

    Google Scholar 

  16. Jabbar, M.A., Deekshatulu, B.L., Chandra, P.: Prediction of heart disease using random forest and feature subset selection. In: Innovations in Bio-Inspired Computing and Applications, pp. 187–196. Springer, Cham (2016)

    Google Scholar 

  17. Djellali, H., Zine, N.G., Azizi, N.: Two stages feature selection based on filter ranking methods and SVMRFE on medical applications. In: Modelling and Implementation of Complex Systems, pp. 281–293. Springer, Cham (2016)

    Google Scholar 

  18. Huda, S., Yearwood, J., Jelinek, H.F., Hassan, M.M., Fortino, G., Buckland, M.: A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis. IEEE Access 4, 9145–9154 (2016)

    Article  Google Scholar 

  19. Liu, X., Wang, X., Su, Q., Zhang, M., Zhu, Y., Wang, Q., Wang, Q.: A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput. Math. Methods Med. (2017)

    Google Scholar 

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

    MATH  Google Scholar 

  21. Tsanas, A., Little, M.A., McSharry, P.E.: A simple filter benchmark for feature selection. J. Mach. Learn. Res. 1, 1–24 (2010)

    Google Scholar 

  22. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 8, 1226–1238 (2005)

    Article  Google Scholar 

  23. Cervante, L., Xue, B., Zhang, M., Shang, L.: Binary particle swarm optimisation for feature selection: A filter based approach. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2012)

    Google Scholar 

  24. Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence, pp. 388–391. IEEE (1995)‏

    Google Scholar 

  25. Kira, K., Rendell, L.A.: The feature selection problem: traditional methods and a new algorithm. In: AAAI, vol. 2, pp. 129–134, July 1992‏

    Google Scholar 

  26. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  27. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)

    Article  Google Scholar 

  28. Wagacha, P.W.: Induction of decision trees. Found. Learn. Adapt. Syst. 12, 1–14 (2003)

    Google Scholar 

  29. Kleinbaum, D.G., Dietz, K., Gail, M., Klein, M., Klein, M.: Logistic regression. Springer, New York (2002)

    Google Scholar 

  30. Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Australasian Joint Conference on Artificial Intelligence, pp. 1015–1021. Springer, Heidelberg (2006)

    Google Scholar 

  31. Tharwat, A.: Classification assessment methods. Appl. Comput. Inf. (2018)

    Google Scholar 

  32. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Science, Minia University, El-Minia, Egypt

    Heba Mamdouh Farghaly, Abdelmgeid A. Ali & Tarek Abd El-Hafeez

  2. Computer Science Unit, Deraya University, El-Minia, Egypt

    Tarek Abd El-Hafeez

Authors
  1. Heba Mamdouh Farghaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelmgeid A. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tarek Abd El-Hafeez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heba Mamdouh Farghaly .

Editor information

Editors and Affiliations

  1. Tomas Bata University, ZlĂ­n, Czech Republic

    Radek Silhavy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Farghaly, H.M., Ali, A.A., El-Hafeez, T.A. (2020). Developing an Efficient Method for Automatic Threshold Detection Based on Hybrid Feature Selection Approach. In: Silhavy, R. (eds) Artificial Intelligence and Bioinspired Computational Methods. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1225. Springer, Cham. https://doi.org/10.1007/978-3-030-51971-1_5

Download citation

Publish with us

Policies and ethics

Search

Navigation