Skip to main content
Log in

Fuzzy decision function estimation using fuzzified particle swarm optimization

International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Present paper reports an upgrade of particle swarm optimization (PSO) algorithm for fuzzy environment by the definition of the particles as fuzzy numbers and reformulating their motion by fuzzy equations. The proposed fuzzified PSO is used to construct a set of fuzzy hyperplanes in the feature space to distinguish different classes. Fuzzy decision hyperplane assign a fuzzy membership to each sample rather than allocating to a specific class. Also the weight vector of fuzzy decision hyperplane is a set of fuzzy numbers. The proposed fuzzy classifier is called fuzzified particle swarm classifier (FPS-classifier) and its performance is evaluated by some artificial and well known benchmarks data sets.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Askari H, Zahiri S-H (2012) Decision function estimation using intelligent gravitational search algorithm. Int J Mach Learn Cybern 3:163–172

    Article  Google Scholar 

  2. Bahrololoum A, Nezamabadi-pour H, Bahrololoum H, Saeed M (2012) A prototype classifier based on gravitational search algorithm. Appl Soft Comput 12(2):819–825

    Article  Google Scholar 

  3. Bandyopadhyay S, Murthy CA, Pal SK (1999) Theoretical performance of genetic pattern classifier. J Franklin Inst 336(3):387–422

    Article  MATH  MathSciNet  Google Scholar 

  4. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, New York

    MATH  Google Scholar 

  5. Hao PY (2008) Fuzzy one-class support vector machines. Fuzzy Sets Syst 159(18):2317–2336

    Article  MATH  MathSciNet  Google Scholar 

  6. He YL, Wang XZ, Huang JZX (2016) Fuzzy nonlinear regression analysis using a random weight network. Inf Sci 364–365(10):222–240

    Article  Google Scholar 

  7. Hrivastri S, Deshmukh R (2014) Data classification particle swarm optimization and gravitational search algorithm. Int J Innovat Res Sci Eng Technol (IJIRSET) 3(2):9734–9741

    Google Scholar 

  8. A-b Ji, Chen S, Hua Q (2014) Fuzzy classifier based on fuzzy support vector machine. J Intell Fuzzy Syst 26(1):421–430

    MATH  MathSciNet  Google Scholar 

  9. Kaufmann M (2015) Inductive fuzzy classification in marketing analytics. Springer International Publishing

  10. Keller J, Gray M, Givens J (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 15:580–585

    Article  Google Scholar 

  11. Keller JM, Hunt DJ (1985) Incorporating fuzzy membership functions into the perceptron algorithm. Pattern Anal Mach Intell IEEE Trans PAMI 7(6):693–699

    Article  Google Scholar 

  12. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Paper presented at the proceedings of the IEEE international conference on neural networks

  13. Liu B (1998) Minimax chance constrained programming models for fuzzy decision systems. Inf Sci 112:25–38

    Article  MATH  MathSciNet  Google Scholar 

  14. Z-g Liu, Pan Q, Dezert J, Mercier G (2014) Credal classification rule for uncertain data based on belief functions. Pattern Recogn 47(7):2532–2541

    Article  Google Scholar 

  15. Ming M, Friedman M, Kandel A (1999) A new fuzzy arithmetic. Fuzzy Sets Syst 108:83–90

    Article  MATH  MathSciNet  Google Scholar 

  16. Rahgooy T, Yazdi HS, Monsefi R (2009) Fuzzy complex system of linear equations applied to circuit analysis. Int J Comput Electr Eng 1(5):537–538

    Google Scholar 

  17. Salimi J, Ghadiri N, Afrabandpey H (2015) Fuzzy least squares twin support vector machines. CoRR abs/1505.05451:1–16

  18. Shahraki H, Zahiri S-H (2013) Design and simulation of an RF MEMS switch for removing the self: actuation and latching phenomena using PSO method. Iran J Electr Comput Eng 12:56–63

    Google Scholar 

  19. Shahraki H, Zahiri S-H (2015) Classification of trapezoidal fuzzy data based on heuristic classifiers. Kasmera J 43:128–144

    Google Scholar 

  20. Shahraki H, Zahiri S-H (2015) Particle swarm classifier for fuzzy data sets. In: Paper presented at the artificial intelligence and signal processing (AISP)

  21. Simpson PK (1992) Fuzzy min-max neural networks. I. Classification. Neural Netw IEEE Trans 3(5):776–786

    Article  Google Scholar 

  22. Wang XZ (2015) Uncertainty in learning from big data-editorial. J Intell Fuzzy Syst 28(5):2329–2330

    Article  Google Scholar 

  23. Wang XZ, Ashfaq RAR, Fu AM (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196

    Article  MathSciNet  Google Scholar 

  24. Wang XZ, He YL, Dong LC, Zhao HY (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

  25. Wang XZ, Xing HJ, Li QH, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654

    Article  Google Scholar 

  26. Werro N (2015) Fuzzy classification of online customers. Springer International Publishing

  27. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  28. Zahiri S-H (2012) Classification rule discovery using learning automata. Int J Mach Learn Cybernet 3:205–213

    Article  Google Scholar 

  29. Zahiri S-H, Mashhadi HR, Seyedin S-A (2005) Intelligent and robust Genetic algorithm based classifier. Iran J Electr Electron Eng 1(3):1–9

    Google Scholar 

  30. Zahiri S-H, Seyedin S-A (2007) Swarm intelligence based classifiers. J Franklin Inst 344(5):362–376

    Article  MATH  Google Scholar 

  31. Zahiri S-H, Seyedin S-A (2009) Using multi-objective Particle Swarm optimization for designing novel classifiers. Swarm Intell Multi-object Probl Data Mining 242:65–92

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hadi Shahraki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahraki, H., Zahiri, SH. Fuzzy decision function estimation using fuzzified particle swarm optimization. Int. J. Mach. Learn. & Cyber. 8, 1827–1838 (2017). https://doi.org/10.1007/s13042-016-0561-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-016-0561-8

Keywords

Navigation