Skip to main content
SpringerLink
Log in

Metaheuristics for Feature Selection: A Comprehensive Comparison Using Opytimizer

  • Chapter
  • First Online:
Benchmarks and Hybrid Algorithms in Optimization and Applications

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

  • 154 Accesses

Abstract

The exponential growth of data generation was essential to the recent advances in machine learning. However, processing such an amount of data imposes challenges and constraints, such as redundant and irrelevant information, which increases the computational burden and usually decreases the learning efficiency and effectiveness. In this scenario, feature selection approaches are suitable for data preprocessing and optimization, particularly the ones based on metaheuristic optimization techniques. This chapter provides a comprehensive comparison among metaheuristic-based architectures for feature selection, as well as a hands-on tutorial followed by a case study using the Opytimizer(https://github.com/gugarosa/opytimizer) framework and the Naïve Bayes classifier.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

Notes

  1. 1.

    Although one can use any technique, we opted to use the Naïve Bayes, which is a simple probabilistic-based classifier.

  2. 2.

    Note that the final evaluation discards the validation set and uses only the training and testing ones.

  3. 3.

    The source code is available at https://github.com/gugarosa/mh_feature_selection.

References

  1. Agarwal V, Bhanot S (2015) Firefly inspired feature selection for face recognition. In: 2015 Eighth international conference on contemporary computing (IC3). IEEE, Noida, India, pp 257–262. https://doi.org/10.1109/IC3.2015.7346689. http://ieeexplore.ieee.org/document/7346689/

  2. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

    Google Scholar 

  3. Botchway RK, Yadav V, Kominkova ZO, Senkerik R (2022) Text-based feature selection using binary particle swarm optimization for sentiment analysis. In: 2022 international conference on electrical, computer and energy technologies (ICECET). IEEE, Prague, Czech Republic, pp 1–4. https://doi.org/10.1109/ICECET55527.2022.9872823. https://ieeexplore.ieee.org/document/9872823/

  4. Chen H, Hou Q, Han L, Hu Z, Ye Z, Zeng J, Yuan J (2019) Distributed text feature selection based on bat algorithm optimization. In: 2019 10th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS). IEEE, Metz, France, pp 75–80. https://doi.org/10.1109/IDAACS.2019.8924308. https://ieeexplore.ieee.org/document/8924308/

  5. Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml

  6. Emary E, Zawbaa HM, Ghany KKA, Hassanien AE, Parv B (2015) Firefly optimization algorithm for feature selection. In: Proceedings of the 7th Balkan conference on informatics conference. ACM, Craiova Romania, pp 1–7. https://doi.org/10.1145/2801081.2801091. https://dl.acm.org/doi/10.1145/2801081.2801091

  7. Ergun E, Aydemir O (2020) Firefly algorithm based feature selection for EEG signal classification. In: 2020 medical technologies congress (TIPTEKNO). IEEE, Antalya, pp 1–4. https://doi.org/10.1109/TIPTEKNO50054.2020.9299273. https://ieeexplore.ieee.org/document/9299273/

  8. Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701

    Article  MATH  Google Scholar 

  9. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92

    Article  MathSciNet  MATH  Google Scholar 

  10. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley Publishing Company, New York

    MATH  Google Scholar 

  11. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872

    Article  Google Scholar 

  12. Kamel SR, Yaghoubzadeh R (2021) Feature selection using grasshopper optimization algorithm in diagnosis of diabetes disease. Inf Med Unlocked 26:100–707. https://doi.org/10.1016/j.imu.2021.100707. https://linkinghub.elsevier.com/retrieve/pii/S2352914821001908

  13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

    Google Scholar 

  14. Mahboob AS, Moghaddam MRO (2020) An anomaly-based intrusion detection system using butterfly optimization algorithm. In: 2020 6th Iranian conference on signal processing and intelligent systems (ICSPIS). IEEE, Mashhad, Iran, pp 1–6. https://doi.org/10.1109/ICSPIS51611.2020.9349537. https://ieeexplore.ieee.org/document/9349537/

  15. Muthulakshmi M, Kavitha G, Aishwarya N (2022) Multi-objective butterfly optimization for feature and classifier parameter’s selection in diagnosis of heart failure types using CMR images. In: 2022 IEEE global conference on computing, power and communication technologies (GlobConPT). IEEE, New Delhi, India, pp 01–06. https://doi.org/10.1109/GlobConPT57482.2022.9938325. https://ieeexplore.ieee.org/document/9938325/

  16. Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) Bba: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI conference on graphics, patterns and images, pp 291–297. https://doi.org/10.1109/SIBGRAPI.2012.47

  17. Nemenyi P (1963) Distribution-free multiple comparisons. Princeton University

    Google Scholar 

  18. Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. http://arxiv.org/abs/1107.4557. ArXiv:1107.4557 [cs]

  19. Papa JP, Falcão AX, De Albuquerque VHC, Tavares JMR (2012) Efficient supervised optimum-path forest classification for large datasets. Pattern Recognit 45(1):512–520

    Article  Google Scholar 

  20. Papa JP, Falcao AX, Suzuki CT (2009) Supervised pattern classification based on optimum-path forest. Int J Imaging Syst Technol 19(2):120–131

    Article  Google Scholar 

  21. Rajamohana SP, Umamaheswari K, Abirami B (2017) Adaptive binary flower pollination algorithm for feature selection in review spam detection. In: 2017 international conference on innovations in green energy and healthcare technologies (IGEHT). IEEE, Coimbatore, pp 1–4. https://doi.org/10.1109/IGEHT.2017.8094094. http://ieeexplore.ieee.org/document/8094094/

  22. Rodrigues D, Pereira LA, Nakamura RY, Costa KA, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest. Expert Syst Appl 41(5):2250–2258

    Google Scholar 

  23. Rodrigues D, Pereira LAM, Almeida TNS, Papa JP, Souza AN, Ramos CCO, Yang, X.-S.: BCS: a binary cuckoo search algorithm for feature selection. In: 2013 IEEE international symposium on circuits and systems (ISCAS2013). IEEE, Beijing, pp 465–468. https://doi.org/10.1109/ISCAS.2013.6571881. http://ieeexplore.ieee.org/document/6571881/

  24. Rodrigues D, Silva GF, Papa JP, Marana AN, Yang XS (2016) Eeg-based person identification through binary flower pollination algorithm. Expert Syst Appl 62:81–90. https://doi.org/10.1016/j.eswa.2016.06.006. https://www.sciencedirect.com/science/article/pii/S0957417416302871

  25. Rodrigues D, Yang XS, de Souza AN, Papa JP (2015) Binary flower pollination algorithm and its application to feature selection. In: Yang XS (ed) Recent advances in swarm intelligence and evolutionary computation, vol. 585. Springer International Publishing, Cham, pp 85–100. https://doi.org/10.1007/978-3-319-13826-8_5. https://link.springer.com/10.1007/978-3-319-13826-8_5. Series Title: Studies in Computational Intelligence

  26. de Rosa GH, Rodrigues D, Papa JP (2019) Opytimizer: a nature-inspired python optimizer. arXiv:1912.13002

  27. Samadiani N, Moameri S (2017) Diagnosis of coronary artery disease using cuckoo search and genetic algorithm in single photon emision computed tomography images. In: 2017 7th international conference on computer and knowledge engineering (ICCKE). IEEE, Mashhad, pp 314–318. https://doi.org/10.1109/ICCKE.2017.8167898. http://ieeexplore.ieee.org/document/8167898/

  28. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Google Scholar 

  29. Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE symposium on computational intelligence for security and defense applications. IEEE, Ottawa, ON, Canada, pp 1–6. https://doi.org/10.1109/CISDA.2009.5356528. http://ieeexplore.ieee.org/document/5356528/

  30. Tubishat M, Alswaitti M, Mirjalili S, Al-Garadi MA, Alrashdan MT, Rana TA (2020) Dynamic butterfly optimization algorithm for feature selection. IEEE Access 8:194,303–194,314. https://doi.org/10.1109/ACCESS.2020.3033757. https://ieeexplore.ieee.org/document/9239279/

  31. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74

    Google Scholar 

  32. Yang XS (2012) Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer, pp 240–249

    Google Scholar 

  33. Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214

    Google Scholar 

  34. Yang XS, Gandomi AH (2011) Bat algorithm: a novel approach for global engineering optimization. Eng Comput

    Google Scholar 

  35. Yang XS, He X (2013) Firefly algorithm: recent advances and applications. arXiv:1308.3898

Download references

Author information

Authors and Affiliations

  1. Department of Computing, São Paulo State University, Bauru, Brazil

    Douglas Rodrigues, Leandro Aparecido Passos, Luiz Fernando Merli de Oliveira Sementille, Mateus Roder, Gustavo Henrique de Rosa & João Paulo Papa

Authors
  1. Douglas Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leandro Aparecido Passos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luiz Fernando Merli de Oliveira Sementille
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mateus Roder
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gustavo Henrique de Rosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. João Paulo Papa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to João Paulo Papa .

Editor information

Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University, London, UK

    Xin-She Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rodrigues, D., Passos, L.A., de Oliveira Sementille, L.F.M., Roder, M., de Rosa, G.H., Papa, J.P. (2023). Metaheuristics for Feature Selection: A Comprehensive Comparison Using Opytimizer. In: Yang, XS. (eds) Benchmarks and Hybrid Algorithms in Optimization and Applications. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3970-1_6

Download citation

Publish with us

Policies and ethics

Search

Navigation