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Recent Advances on Hybrid Approaches for Designing Intelligent Systems pp 113–123Cite as

Uncertainty-Based Information Granule Formation

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 547))

Abstract

A new technique for forming information granules is shown in this chapter. Based on the theory of uncertainty-based information, an approach is proposed which forms Interval Type-2 Fuzzy information granules. This approach captures multiple evaluations of uncertainty from taken samples and uses these models to measure the uncertainty from the difference in these. The proposed approach is tested through multiple benchmark datasets: iris, wine, glass, and a 5th order curve identification.

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References

  1. Bargiela, A., Pedrycz, W.: Toward a theory of granular computing for human-centered information processing. IEEE Trans. Fuzzy Syst. 16(2), 320–330 (2008)

    Article  Google Scholar 

  2. Pedrycz, W.: Granular computing: the emerging paradigm. J. Uncertain Syst. 1(1), 38–61 (2007)

    Google Scholar 

  3. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  4. Zhang, L.Z.L., Zhang, B.Z.B.: Quotient space based multi-granular computing, vol. 1 (2005)

    Google Scholar 

  5. Pedrycz, W., Vukovich, G.: Granular computing with shadowed sets. Int. J. Intell. Syst. 17(2), 173–197 (2002)

    Article  MATH  Google Scholar 

  6. Zhang, Y.Z.Y., Zhu, X.Z.X., Huang, Z.H.Z.: Fuzzy Sets Based Granular Logics for Granular Computing. (2009)

    Google Scholar 

  7. Yao, J.T.: Information granulation and granular relationships. In: 2005 IEEE International Conference on Granular Computing, vol. 1 (2005)

    Google Scholar 

  8. Yu, F.Y.F., Cai, R.C.R.: Optimized fuzzy information granulation of temporal data. In: Fuzzy Systems and Knowledge Discovery (FSKD), vol. 1 (2010)

    Google Scholar 

  9. Yao, J., Yao, Y.Y.: Information granulation for web-based information support systems. In: Proceedings of SPIE, pp. 138–146 (2003)

    Google Scholar 

  10. Park, H.-S., Chung, Y.-D., Oh, S.-K., Pedrycz, W., Kim, H.-K.: Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet. Eng. Appl. Artif. Intell. 24(3), 543–554 (2011)

    Article  Google Scholar 

  11. Sanchez, M.A., Castro, J.R., Perez-Ornelas, F., Castillo, O.: A hybrid method for IT2 TSK formation based on the principle of justifiable granularity and PSO for spread optimization. In: 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 1268–1273 (2013)

    Google Scholar 

  12. Sanchez, M.A., Castro, J.R., Castillo, O.: Formation of general type-2 Gaussian membership functions based on the information granule numerical evidence. In: 2013 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA), pp. 1–6 (2013)

    Google Scholar 

  13. Klir, G.J.: Uncertainty and Information: Foundations of Generalized Information Theory, p. 499. Wiley-IEEE Press, New Jersey (2005)

    Book  Google Scholar 

  14. Klir, G.J., Wierman, M.J.: Uncertainty-Based Information, vol. 15. Physica-Verlag HD, Heidelberg (1999)

    Book  MATH  Google Scholar 

  15. Weise, K., Woger, W.: A Bayesian theory of measurement uncertainty. Meas. Sci. Technol. 3, 1–11 (1992)

    Article  Google Scholar 

  16. J. C. F. G. I. M. JCGM: Evaluation of measurement data—guide to the expression of uncertainty in measurement. Int. Organ. Stand. Geneva ISBN. 50, 134 (2008)

    Google Scholar 

  17. Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994)

    Article  MathSciNet  Google Scholar 

  18. Jang, J.-S.R.: Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In: Proceedings of the Ninth National Conference on Artificial Intelligence, pp. 762–767 (1991)

    Google Scholar 

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

    Article  Google Scholar 

  20. Yang, X.-S., Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214 (2009)

    Google Scholar 

  21. Frank A., Asuncion, A.: UCI Machine Learning Repository. University of California Irvine School of Information, School of Information and Computer Sciences, University of California, Irvine, p. 0 (2010) (vol. 2008, no. 14/8)

    Google Scholar 

  22. Daneshgar, A., Javadi, R., Razavi, B.S.: Clustering using isoperimetric number of trees. Pattern Recognit. 46(12), 3371–3382 (2012)

    Article  Google Scholar 

  23. David, G., Averbuch, A.: SpectralCAT: categorical spectral clustering of numerical and nominal data. Pattern Recognit. 45(1), 416–433 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  24. Thi, L., An, H., Hoai, L., Dinh, P.: New and efficient DCA based algorithms for minimum sum-of-squares clustering. Pattern Recognit. 47, 388–401 (2014)

    Article  Google Scholar 

  25. Castillo, O., Huesca, G., Valdez, F.: Evolutionary computing for topology optimization of type-2 fuzzy controllers. Stud. Fuzziness Soft Comput. 208, 163–178 (2008)

    Article  Google Scholar 

  26. Castillo, O., Melin, P.: Type-2 Fuzzy Logic: Theory and Applications. Springer-Verlag, Heidelberg (2008)

    Google Scholar 

  27. Castillo, O., Aguilar, L.T., Cazarez-Castro, N.R., Cardenas, S.: Systematic design of a stable type-2 fuzzy logic controller. Appl. Soft Comput. J. 8, 1274–1279 (2008)

    Article  Google Scholar 

  28. Castillo, O., Martinez-Marroquin, R., Melin, P., Valdez, F., Soria, J.: Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Inf. Sci. 192, 19–38 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Castro, J.R., Castillo, O., Melin, P.: An interval type-2 fuzzy logic toolbox for control applications. In: Proceedings of FUZZ-IEEE 2007, London, pp. 1–6 (2007)

    Google Scholar 

  31. Castro, J.R., Castillo, O., Martinez, L.G.: Interval type-2 fuzzy logic toolbox. Eng. Lett. 15(1), 14 (2007)

    Google Scholar 

  32. Hidalgo, D., Castillo, O., Melin, P.: Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms. Inf. Sci. 179(13), 2123–2145 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Montiel, O., Sepulveda, R., Melin, P., Castillo, O., Porta Garcia, M., Meza Sanchez I.: Performance of a simple tuned fuzzy controller and a PID controller on a DC motor. In: FOCI 2007 pp. 531–537

    Google Scholar 

  36. Rubio, E., Castillo, O.: Optimization of the interval type-2 fuzzy C-means using particle swarm optimization. In: Proceedings of NABIC 2013, pp. 10–15, Fargo, USA (2013)

    Google Scholar 

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Acknowledgments

We thank the MyDCI program of the Division of Graduate Studies and Research, UABC, and Tijuana Institute of Technology the financial support provided by our sponsor CONACYT contract grant number: 314258.

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Authors and Affiliations

  1. Autonomous University of Baja California, Tijuana, Mexico

    Mauricio A. Sanchez & Juan R. Castro

  2. Tijuana Institutes of Technology, Tijuana, Mexico

    Oscar Castillo

Authors
  1. Mauricio A. Sanchez
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  2. Oscar Castillo
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  3. Juan R. Castro
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Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

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

    Oscar Castillo

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

    Patricia Melin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Sanchez, M.A., Castillo, O., Castro, J.R. (2014). Uncertainty-Based Information Granule Formation. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-05170-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05169-7

  • Online ISBN: 978-3-319-05170-3

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