Have a personal or library account? Click to login

GrNFS: A Granular Neuro–Fuzzy System for Regression in Large Volume Data

Open Access
|Sep 2021

References

  1. Ahmed, M.M. and Isa, N.A.M. (2017). Knowledge base to fuzzy information granule: A review from the interpretability-accuracy perspective, Applied Soft Computing 54: 121–140.10.1016/j.asoc.2016.12.055
  2. Alcalá, R., Alcalá-Fdez, J., Casillas, J., Cordón, O. and Herrera, F. (2006). Hybrid learning models to get the interpretability-accuracy trade-off in fuzzy modeling, Soft Computing 10(9): 717–734.10.1007/s00500-005-0002-1
  3. Alonso, J.M. and Magdalena, L. (2011). Special issue on interpretable fuzzy systems, Information Sciences 181(20): 4331–4339.10.1016/j.ins.2011.07.001
  4. Atanassov, K.T. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1): 87–96.10.1016/S0165-0114(86)80034-3
  5. Bargiela, A. and Pedrycz, W. (2006). The roots of granular computing, 2006 IEEE International Conference on Granular Computing, Atlanta, USA, pp. 806–809.
  6. Bisi, C., Chiaselotti, G., Ciucci, D., Gentile, T. and Infusino, F.G. (2017). Micro and macro models of granular computing induced by the indiscernibility relation, Information Sciences 388–389: 247–273.10.1016/j.ins.2017.01.023
  7. Botta, A., Lazzerini, B., Marcelloni, F. and Stefanescu, D.C. (2009). Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index, Soft Computing 13(5): 437–449.10.1007/s00500-008-0360-6
  8. Ciucci, D. (2016). Orthopairs and granular computing, Granular Computing 1: 159–170.10.1007/s41066-015-0013-y
  9. Cpałka, K., Łapa, K., Przybył, A. and Zalasiński, M. (2014). A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neuro-computing 135: 203–217.10.1016/j.neucom.2013.12.031
  10. Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters, Journal Cybernetics 3(3): 32–57.10.1080/01969727308546046
  11. Evsukoff, A.G., Galichet, S., de Lima, B.S. and Ebecken, N.F. (2009). Design of interpretable fuzzy rule-based classifiers using spectral analysis with structure and parameters optimization, Fuzzy Sets and Systems 160(7): 857–881.10.1016/j.fss.2008.08.010
  12. Gacto, M.J., Alcalá, R. and Herrera, F. (2011). Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences 181(20): 4340–4360.10.1016/j.ins.2011.02.021
  13. Herrera, L.J., Pomares, H., Rojas, I., Valenzuela, O. and Prieto, A. (2005). TASE, a Taylor series-based fuzzy system model that combines interpretability and accuracy, Fuzzy Sets and Systems 153(3): 403–427.10.1016/j.fss.2005.01.012
  14. Hu, X., Pedrycz, W., Wu, G. and Wang, X. (2017). Data reconstruction with information granules: An augmented method of fuzzy clustering, Applied Soft Computing 55: 523–532.10.1016/j.asoc.2017.02.014
  15. Juang, C.-F. and Chen, C.-Y. (2013). Data-driven interval type-2 neural fuzzy system with high learning accuracy and improved model interpretability, IEEE Transactions on Cybernetics 43(6): 1781–1795.10.1109/TSMCB.2012.2230253
  16. Juang, C.-F. and Lin, C.-T. (1998). An online self-constructing neural fuzzy inference network and its applications, IEEE Transactions on Fuzzy Systems 6(1): 12–32.10.1109/91.660805
  17. Juang, C.F. and Tsao, Y.W. (2008). A type-2 self-organizing neural fuzzy system and its FPGA implementation, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38(6): 1537–1548.10.1109/TSMCB.2008.927713
  18. Keet, C.M. (2008). A Formal Theory of Granularity, PhD thesis, Free University of Bozen-Bolzano, Bolzano.
  19. Leski, J. (2008). Neuro-Fuzzy Systems, Scientific and Engineering Publishers WNT, Warsaw, (in Polish).
  20. Lorenz, E.N. (1963). Deterministic nonperiodic flow, Journal of the Atmospheric Sciences 20(2): 130–141.10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2
  21. Mackey, M.C. and Glass, L. (1977). Oscillation and chaos in physiological control systems, Science 197(4300): 287–289.10.1126/science.267326
  22. Mendel, J.M. (2004). Computing derivatives in interval type-2 fuzzy logic systems, IEEE Transactions on Fuzzy Systems 12(1): 84–98.10.1109/TFUZZ.2003.822681
  23. Pawlak, Z. (1996). Rough sets, rough relations and rough functions, Fundamenta Informaticae 27(2): 103–108.10.3233/FI-1996-272301
  24. Pedrycz, A., Hirota, K., Pedrycz, W. and Dong, F. (2012). Granular representation and granular computing with fuzzy sets, Fuzzy Sets and Systems 203: 17–32.10.1016/j.fss.2012.03.009
  25. Pedrycz, W. (1998). Shadowed sets: Representing and processing fuzzy sets, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 28(1): 103–109.10.1109/3477.65858418255928
  26. Pedrycz, W. (2013). Granular Computing: Analysis and Design of Intelligent Systems, CRC Press, Boca Raton.10.1201/b14862
  27. Pedrycz, W. and Gomide, F. (2007). Fuzzy Systems Engineering: Toward Human-Centric Computing, John Wiley, Hoboken.10.1002/9780470168967
  28. Pedrycz, W., Hmouz, R.A., Balamash, A.S. and Morfeq, A. (2015a). Hierarchical granular clustering: An emergence of information granules of higher type and higher order, IEEE Transactions on Fuzzy Systems 23(6): 2270–2283.10.1109/TFUZZ.2015.2417896
  29. Pedrycz, W. and Homenda, W. (2013). Building the fundamentals of granular computing: A principle of justifiable granularity, Applied Soft Computing 13(10): 4209–4218.10.1016/j.asoc.2013.06.017
  30. Pedrycz, W., Succi, G., Sillitti, A. and Iljazi, J. (2015b). Data description: A general framework of information granules, Knowledge-Based Systems 80: 98–108.10.1016/j.knosys.2014.12.030
  31. Qian, Y.H., Liang, J.Y., Yao, Y.Y. and Dang, C.Y. (2010). MGRS: A multi-granulation rough set, Information Sciences 180(6): 949–970.10.1016/j.ins.2009.11.023
  32. Qian, Y., Liang, J., Wu, W.-Z. and Dang, C. (2011). Information granularity in fuzzy binary GrC model, IEEE Transactions on Fuzzy Systems 19(2): 253–264.10.1109/TFUZZ.2010.2095461
  33. Reyes-Galaviz, O.F. and Pedrycz, W. (2015). Granular fuzzy models: Analysis, design, and evaluation, International Journal of Approximate Reasoning 64: 1–19.10.1016/j.ijar.2015.06.005
  34. Rössler, O.E. (1976). An equation for continuous chaos, Physics Letters A 57(5): 397–398.10.1016/0375-9601(76)90101-8
  35. Salehi, S., Selamat, A. and Fujita, H. (2015). Systematic mapping study on granular computing, Knowledge-Based Systems 80: 78–97.10.1016/j.knosys.2015.02.018
  36. Shifei, D., Li, X., Hong, Z. and Liwen, Z. (2010). Research and progress of cluster algorithms based on granular computing, International Journal of Digital Content Technology and Its Applications 4(5): 96–104.10.4156/jdcta.vol4.issue5.11
  37. Siminski, K. (2014). Neuro-fuzzy system with weighted attributes, Soft Computing 18(2): 285–297.10.1007/s00500-013-1057-z
  38. Siminski, K. (2015). Rough subspace neuro-fuzzy system, Fuzzy Sets and Systems 269: 30–46.10.1016/j.fss.2014.07.003
  39. Siminski, K. (2017). Interval type-2 neuro-fuzzy system with implication-based inference mechanism, Expert Systems with Applications 79C: 140–152.10.1016/j.eswa.2017.02.046
  40. Siminski, K. (2019). NFL—free library for fuzzy and neuro-fuzzy systems, in S. Kozielski et al. (Eds), Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis, Springer International Publishing, Cham, pp. 139–150.10.1007/978-3-030-19093-4_11
  41. Siminski, K. (2020). GrFCM—Granular clustering of granular data, in A. Gruca et al. (Eds), Man–Machine Interactions 6, Springer, Cham, pp. 111–121.10.1007/978-3-030-31964-9_11
  42. Siminski, K. (2021). An outlier-robust neuro-fuzzy system for classification and regression, International Journal of Applied Mathematics and Computer Science 31(2): 303–319, DOI: 10.34768/amcs-2021-0021.
  43. Skowron, A., Jankowski, A. and Dutta, S. (2016). Interactive granular computing, Granular Computing 1: 95–113.10.1007/s41066-015-0002-1
  44. Sugeno, M. and Kang, G.T. (1988). Structure identification of fuzzy model, Fuzzy Sets ans Systems 28(1): 15–33.10.1016/0165-0114(88)90113-3
  45. Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics 15(1): 116–132.10.1109/TSMC.1985.6313399
  46. Wang, D., Pedrycz, W. and Li, Z. (2019). Granular data aggregation: An adaptive principle of the justifiable granularity approach, IEEE Transactions on Cybernetics 49(2): 1–10.10.1109/TCYB.2017.2774831
  47. Yang, X., Li, T., Liu, D. and Fujita, H. (2019). A temporal-spatial composite sequential approach of three-way granular computing, Information Sciences 486: 171–189.10.1016/j.ins.2019.02.048
  48. Yao, J.T., Vasilakos, A.V. and Pedrycz, W. (2013). Granular computing: Perspectives and challenges, IEEE Transactions on Cybernetics 43(6): 1977–1989.10.1109/TSMCC.2012.2236648
  49. Yao, Y. (2007). The art of granular computing, in M. Kryszkiewicz et al. (Eds), Rough Sets and Intelligent Systems Paradigms, Springer, Berlin/Heidelberg, pp. 101–112.10.1007/978-3-540-73451-2_12
  50. Yao, Y. (2008). Granular computing: Past, present and future, 2008 IEEE International Conference on Granular Computing, GrC 2008, Hangzhou, China, pp. 80–85.
  51. Yao, Y. (2018). Three-way decision and granular computing, International Journal of Approximate Reasoning 103: 107–123.10.1016/j.ijar.2018.09.005
  52. Yao, Y. (2020). Three-way granular computing, rough sets, and formal concept analysis, International Journal of Approximate Reasoning 116: 106–125.10.1016/j.ijar.2019.11.002
  53. Yao, Y. and Zhong, N. (2007). Granular computing, in B.W. Wah (Ed.), Wiley Encyclopedia of Computer Science and Engineering, Wiley, Hoboken.10.1002/9780470050118.ecse468
  54. Yen, J., Wang, L. and Gillespie, C. W. (1998). Improving the interpretability of TSK fuzzy models by combining global learning and local learning, IEEE Transactions on Fuzzy Systems 6(4): 530–537.10.1109/91.728447
  55. Zadeh, L.A. (1965). Fuzzy sets, Information and Control 8: 338–353.10.1016/S0019-9958(65)90241-X
  56. Zadeh, L.A. (1979). Fuzzy sets and information granularity, in N. Gupta et al. (Eds), Advances in Fuzzy Set Theory and Applications, North-Holland Publishing, Amsterdam, pp. 3–18.
  57. Zadeh, L.A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems 90(2): 111–127.10.1016/S0165-0114(97)00077-8
  58. Zadeh, L.A. (2002). From computing with numbers to computing with words—From manipulation of measurements to manipulation of perceptions, International Journal of Applied Mathematics and Computer Science 12(3): 307–324.10.1063/1.1388678
DOI: https://doi.org/10.34768/amcs-2021-0030 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 445 - 459
Submitted on: Mar 25, 2021
Accepted on: Jun 28, 2021
Published on: Sep 27, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2021 Krzysztof Siminski, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.