Skip to main content
SpringerLink
Log in

Feature selection: Comparative Analysis of Binary Metaheuristics and Population Based Algorithm with Adaptive Memory

  • Published:
Programming and Computer Software Aims and scope Submit manuscript

Abstract

The NP-hard feature selection problem is studied. For solving this problem, a population based algorithm that uses a combination of random and heuristic search is proposed. The solution is represented by a binary vector the dimension of which is determined by the number of features in the data set. New solution are generated randomly using the normal and uniform distribution. The heuristic underlying the proposed approach is formulated as follows: the chance of a feature to get into the next generation is proportional to the frequency with which this feature occurs in the best preceding solutions. The effectiveness of the proposed algorithm is checked on 18 known data sets. This algorithm is statistically compared with other similar 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

Fig. 1.

Similar content being viewed by others

REFERENCES

  1. Xue, B., Zhang, M., Browne, W.N., and Yao, X., A survey on evolutionary computation approaches to feature selection, IEEE Trans. Evolutionary Comput., 2016, vol. 20, pp. 606–626.

    Article  Google Scholar 

  2. Labati, R.D., Genovese, A., Munoz, E., Piuri, V., and Scotti, F., Applications of computational intelligence in industrial and environmental scenarios, Studies Comput. Intell., 2018, vol. 756, pp. 29–46.

    Google Scholar 

  3. de la Iglesia, B., Evolutionary computation for feature selection in classification problems, WIREs Data Mining and Knowledge Discovery, 2013, vol. 3, pp. 381–407.

    Article  Google Scholar 

  4. Kohavi, R. and John, G.H., Wrappers for feature subset selection, Artif. Intell., 1997, vol. 97, pp. 273–324.

    Article  Google Scholar 

  5. Saeys, Y., Inza, I., and Larranaga, P., A review of feature selection techniques in bioinformatics, Bioinformatics, 2007, vol. 23, pp. 2507–2517.

    Article  Google Scholar 

  6. Armanfard, N., Reilly, J.P., and Komeili, M., Logistic localized modeling of the sample space for feature selection and classification, IEEE Trans. Neural Networks Learning Syst., 2018, vol. 29, pp. 1396–1413.

    Article  MathSciNet  Google Scholar 

  7. Yusta, S.C., Different metaheuristic strategies to solve the feature selection problem, Pattern Recognit. Lett., 2009, vol. 30, pp. 525–534.

    Article  Google Scholar 

  8. Hodashinsky, I.A and Mekh, M.A., Fuzzy Classifier Design Using Harmonic Search Methods, Program. Comput. Software, 2017, vol. 43, no. 1, pp. 37–46.

    Article  MathSciNet  Google Scholar 

  9. Mafarja, M. and Mirjalili, S., Whale optimization approaches for wrapper feature selection, Applied Soft Comput., 2018, vol. 62, pp. 441–453.

    Article  Google Scholar 

  10. Djellali, H., Djebbar, A., Zine, N.G., and Azizi, N., Hybrid artificial bees colony and particle swarm on feature selection, Computational Intelligence and Its Applications. CIIA 2018, IFIP Advances in Information and Communication Technology, 2018, vol. 522, pp. 93–105.

    Article  Google Scholar 

  11. Glover, F. and Hanafi, S., Tabu search and finite convergence, Discrete Appl. Math., 2002, vol. 119, pp. 3–36.

    Article  MathSciNet  Google Scholar 

  12. Riley, R.C.L. and Rego, C., Intensification, diversification, and learning via relaxation adaptive memory programming: A case study on resource constrained project scheduling, J. Heuristics, 2018, pp. 1–15.

  13. Omran, M.G.H. and Clerc, M., APS 9: An improved adaptive population-based simplex method for real-world engineering optimization problems, Appl. Intell., 2018, vol. 48, pp. 1596–1608.

    Article  Google Scholar 

  14. Nelder, J. and Mead, R., A simplex method for function minimization, Comput. J., 1965, vol. 7, pp. 308–313.

    Article  MathSciNet  Google Scholar 

  15. Saha, S. and Mukherjee, V., A novel chaos-integrated symbiotic organisms search algorithm for global optimization, Soft Comput., 2018, vol. 22, pp. 3797–3816.

    Article  Google Scholar 

  16. Glantz, S.A., Primer of Biostatistics, New York: McGraw-Hill, 1994.

    MATH  Google Scholar 

Download references

Funding

This work was supported by the Ministry for Science and Education of the Russian Federation, project no. 2.3583.2017/4.6.

Author information

Authors and Affiliations

  1. Tomsk State University of Control Systems and Radio Electronics, 634050, Tomsk, Russia

    I. A. Hodashinsky & K. S. Sarin

Authors
  1. I. A. Hodashinsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. S. Sarin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to I. A. Hodashinsky or K. S. Sarin.

Additional information

Translated by A. Klimontovich

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hodashinsky, I.A., Sarin, K.S. Feature selection: Comparative Analysis of Binary Metaheuristics and Population Based Algorithm with Adaptive Memory. Program Comput Soft 45, 221–227 (2019). https://doi.org/10.1134/S0361768819050037

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0361768819050037

Search

Navigation