Skip to main content
SpringerLink
Log in

Study of the Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Problems

  • Chapter
  • First Online:
Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

Abstract

Fuzzy Logic has been implemented successfully for different kind of problems. However, there is an opportunity for these methods to be improved in the realm of classifications problems. The present paper is focused in a specific application of classification problems, the diagnosis systems, this problem consists in training an intelligent system to learn the relationship between symptoms and diagnosis, this kind of problems are usually based in powerful non-linear methods for example Modular Neural-Networks or complex hybrids models, however, in this paper are applied the Type-1 Takagi Sugeno Fuzzy Systems (TSK) but analyzing the improvement of their performance by increasing the order of the Sugeno polynomial. The conventional Takagi-Sugeno Fuzzy Systems are based in the aggregation of first-order polynomial but it is interesting to observe the effect of increase the order of this polynomial, the TSK Fuzzy Diagnosis Systems are evaluated by their accuracy obtained in ten benchmark dataset of the UCI Dataset Repository, for different kind of diseases and different difficult levels.

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
Hardcover Book
USD 109.99
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

References

  1. C. Caraveo, F. Valdez, O. Castillo, Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Appl. Soft Comput. 43, 131–142 (2016)

    Article  Google Scholar 

  2. O. Castillo, L. Amador-Angulo, J.R. Castro, M. Garcia-Valdez, A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf. Sci. 354, 257–274 (2016)

    Article  Google Scholar 

  3. O. Castillo, P. Melin, A. Alanis, O. Montiel, R. Sepulveda, Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms. Soft. Comput. 15(6), 1145–1160 (2011)

    Article  Google Scholar 

  4. N.R. Cazarez-Castro, L.T. Aguilar, O. Castillo, Designing type-1 and type-2 fuzzy logic controllers via fuzzy lyapunov synthesis for nonsmooth mechanical systems. Eng. Appl. Artif. Intell. 25(5), 971–979 (2012)

    Article  Google Scholar 

  5. L. Cervantes, O. Castillo, Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control. Inf. Sci. 324, 247–256 (2015)

    Article  Google Scholar 

  6. H. Chaoui, M. Khayamy, A.A. Aljarboua, Adaptive interval type-2 fuzzy logic control for PMSM drives with a modified reference frame. IEEE Trans. Ind. Electron. 64(5), 3786–3797 (2017)

    Article  Google Scholar 

  7. A.M. El-Nagar, M. El-Bardini, and N.M. EL-Rabaie, Intelligent control for nonlinear inverted pendulum based on interval type-2 fuzzy PD controller. Alex. Eng. J. 53(1), 23–32 (Mar. 2014)

    Article  Google Scholar 

  8. M.H. Khooban, T. Niknam, M. Sha-Sadeghi, Speed control of electrical vehicles: a time-varying proportional #x2013; integral controller-based type-2 fuzzy logic. IET Sci. Meas. Technol. 10(3), 185–192 (2016)

    Article  Google Scholar 

  9. E. Ontiveros-Robles, P. Melin, and O. Castillo, Comparative analysis of noise robustness of type 2 fuzzy logic controllers. Kybernetika 175–201 (Mar. 2018)

    Google Scholar 

  10. A.I. Roose, S. Yahya, H. Al-Rizzo, Fuzzy-logic control of an inverted pendulum on a cart. Comput. Electr. Eng. 61, 31–47 (2017)

    Article  Google Scholar 

  11. H. Zhou, H. Ying, J. Duan, Adaptive control using interval type-2 fuzzy logic for uncertain nonlinear systems. J. Cent. South Univ. Technol. 18(3), 760 (2011)

    Article  Google Scholar 

  12. P. Melin, E. Ontiveros-Robles, C.I. Gonzalez, J.R. Castro, and O. Castillo, An approach for parameterized shadowed type-2 fuzzy membership functions applied in control applications. Soft Comput. (Sep. 2018)

    Google Scholar 

  13. C.I. Gonzalez, J.R. Castro, O. Mendoza, A. Rodríguez-Díaz, P. Melin, and O. Castillo, Edge detection method based on interval type-2 fuzzy systems for color images, in 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015, pp. 1–6

    Google Scholar 

  14. C.I. Gonzalez, P. Melin, J.R. Castro, O. Castillo, O. Mendoza, Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47, 631–643 (2016)

    Article  Google Scholar 

  15. P. Melin, C.I. Gonzalez, J.R. Castro, O. Mendoza, O. Castillo, Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22(6), 1515–1525 (2014)

    Article  Google Scholar 

  16. O. Mendoza, P. Melin, and G. Licea, A new method for edge detection in image processing using interval type-2 fuzzy logic, in 2007 IEEE International Conference on Granular Computing (GRC 2007), (2007) pp. 151–151

    Google Scholar 

  17. M.H. Khooban, N. Vafamand, A. Liaghat, T. Dragicevic, An optimal general type-2 fuzzy controller for urban traffic network. ISA Trans. 66, 335–343 (2017)

    Article  Google Scholar 

  18. C.F. Juang, K.J. Juang, Circuit implementation of data-driven TSK-type interval type-2 neural fuzzy system with online parameter tuning ability. IEEE Trans. Ind. Electron. 64(5), 4266–4275 (2017)

    Article  Google Scholar 

  19. J. Debnath, D. Majumder, A. Biswas, Air quality assessment using weighted interval type-2 fuzzy inference system. Ecol. Inform. 46, 133–146 (2018)

    Article  Google Scholar 

  20. H. Wang, B. Zheng, S.W. Yoon, H.S. Ko, A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur. J. Oper. Res. 267(2), 687–699 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  21. E. Elyan, M.M. Gaber, A fine-grained random forests using class decomposition: an application to medical diagnosis. Neural Comput. Appl. 27(8), 2279–2288 (2016)

    Article  Google Scholar 

  22. N. MadhuSudana Rao, K. Kannan, X. Gao, D.S. Roy, Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution. Comput. Electr. Eng. 67, 483–496 (Apr. 2018)

    Article  Google Scholar 

  23. W. Sheng, P. Shan, S. Chen, Y. Liu, F.E. Alsaadi, A niching evolutionary algorithm with adaptive negative correlation learning for neural network ensemble. Neurocomputing 247, 173–182 (2017)

    Article  Google Scholar 

  24. I. Saritas, Prediction of breast cancer using artificial neural networks. J. Med. Syst. 36(5), 2901–2907 (2012)

    Article  Google Scholar 

  25. S. Khatri, D. Arora, A. Kumar, Enhancing decision tree classification accuracy through genetically programmed attributes for wart treatment method identification. Procedia Comput. Sci. 132, 1685–1694 (2018)

    Article  Google Scholar 

  26. L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  27. J.R. Castro, O. Castillo, M.A. Sanchez, O. Mendoza, A. Rodríguez-Diaz, P. Melin, Method for higher order polynomial Sugeno Fuzzy inference systems. Inf. Sci. 351, 76–89 (2016)

    Article  MATH  Google Scholar 

  28. J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  29. C. Leal Ramírez, O. Castillo, P. Melin, A. Rodríguez Díaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)

    Article  MathSciNet  Google Scholar 

  30. O. Castillo, P. Melin, Intelligent systems with interval type-2 fuzzy logic. Int. J. Innov. Comput. Inf. Control, 4(4), 771–783 (2008)

    Google Scholar 

  31. G.M. Mendez, O. Castillo, Interval type-2 TSK fuzzy logic systems using hybrid learning algorithm. in The 14th IEEE International Conference on, Fuzzy Systems, 2005. FUZZ’05. (2005), pp. 230–235

    Google Scholar 

  32. E. Rubio, O. Castillo, F. Valdez, P. Melin, C.I. González, G. Martinez, An extension of the fuzzy possibilistic clustering algorithm using type-2 fuzzy logic techniques. Adv. Fuzzy Syst. 2017, 7094046:1–7094046:23 (2017)

    Article  Google Scholar 

  33. P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach. IEEE Trans. Ind. Electr. 48(5), 951–955

    Article  Google Scholar 

  34. L. Aguilar, P. Melin, O. Castillo, Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach. Appl. Soft Comput. 3(3), 209–219 (2003)

    Article  Google Scholar 

  35. P. Melin, O. Castillo, Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory. Appl. Soft Comput. 3(4), 353–362 (2003)

    Article  Google Scholar 

  36. L.B. Goncalves, M.M.B.R. Vellasco, M.A.C. Pacheco, F.J. de Souza, Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36(2), 236–248 (Mar. 2006)

    Article  Google Scholar 

  37. E. Elyan, M.M. Gaber, A genetic algorithm approach to optimising random forests applied to class engineered data. Inf. Sci. 384, 220–234 (2017)

    Article  Google Scholar 

  38. E. Boros, P.L. Hammer, T. Ibaraki, A. Kogan, E. Mayoraz, I. Muchnik, An implementation of logical analysis of data. IEEE Trans. Knowl. Data Eng. 12(2), 292–306 (2000)

    Article  Google Scholar 

  39. B. Ustun, C. Rudin, Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. 102(3), 349–391 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  40. J.A. Morente-Molinera, J. Mezei, C. Carlsson, E. Herrera-Viedma, Improving supervised learning classification methods using multigranular linguistic modeling and fuzzy entropy. IEEE Trans. Fuzzy Syst. 25(5), 1078–1089 (2017)

    Article  Google Scholar 

  41. H.T. Kahraman, A novel and powerful hybrid classifier method: development and testing of heuristic k-nn algorithm with fuzzy distance metric. Data Knowl. Eng. 103, 44–59 (2016)

    Article  Google Scholar 

  42. W.A. Young, S.L. Nykl, G.R. Weckman, D.M. Chelberg, Using Voronoi diagrams to improve classification performances when modeling imbalanced datasets. Neural Comput. Appl. 26(5), 1041–1054 (2015)

    Article  Google Scholar 

  43. K.A. Nugroho, N.A. Setiawan, T.B. Adji, Cascade generalization for breast cancer detection, in 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), (Yogyakarta, Indonesia, 2013), pp. 57–61

    Google Scholar 

  44. A. Zadeh Shirazi, S.J. Seyyed Mahdavi Chabok, Z. Mohammadi, A novel and reliable computational intelligence system for breast cancer detection. Med. Biol. Eng. Comput. 56(5), 721–732 (May 2018)

    Article  Google Scholar 

  45. R. Vidya Banu, N. Nagaveni, Evaluation of a perturbation-based technique for privacy preservation in a multi-party clustering scenario. Inf. Sci. 232, 437–448 (May 2013)

    Article  Google Scholar 

  46. P. Luukka, T. Leppälampi, Similarity classifier with generalized mean applied to medical data. Comput. Biol. Med. 36(9), 1026–1040 (2006)

    Article  Google Scholar 

  47. H. Kahramanli, N. Allahverdi, Design of a hybrid system for the diabetes and heart diseases. Expert Syst. Appl. 35(1), 82–89 (2008)

    Article  Google Scholar 

  48. K. Polat, S. Güneş, A. Arslan, A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Expert Syst. Appl. 34(1), 482–487 (2008)

    Article  Google Scholar 

  49. F. Mansourypoor, S. Asadi, Development of a reinforcement learning-based evolutionary fuzzy rule-based system for diabetes diagnosis. Comput. Biol. Med. 91, 337–352 (2017)

    Article  Google Scholar 

  50. S.B. Akben, Predicting the success of wart treatment methods using decision tree based fuzzy informative images. Biocybern. Biomed. Eng. 38(4), 819–827 (2018)

    Article  Google Scholar 

  51. F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, S. Nahavandi, An expert system for selecting wart treatment method. Comput. Biol. Med. 81, 167–175 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Emanuel Ontiveros-Robles, Patricia Melin & Oscar Castillo

Authors
  1. Emanuel Ontiveros-Robles
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patricia Melin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ontiveros-Robles, E., Melin, P., Castillo, O. (2020). Study of the Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Problems. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_2

Download citation

Publish with us

Policies and ethics

Search

Navigation