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Hybrid Model Based on Rough Sets Theory and Fuzzy Cognitive Maps for Decision-Making

  • Gonzalo Nápoles
  • Isel Grau
  • Koen Vanhoof
  • Rafael Bello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)

Abstract

Decision-making could be defined as the process to choose a suitable decision among a set of possible alternatives in a given activity. It is a relevant subject in numerous disciplines such as engineering, psychology, risk analysis, operations research, etc. However, most real-life problems are unstructured in nature, often involving vagueness and uncertainty features. It makes difficult to apply exact models, being necessary to adopt approximate algorithms based on Artificial Intelligence and Soft Computing techniques. In this paper we present a novel decision-making model called Rough Cognitive Networks. It combines the capability of Rough Sets Theory for handling inconsistent patterns, with the modeling and simulation features of Fuzzy Cognitive Maps. Towards the end, we obtain an accurate hybrid model that allows to solve non-trivial continuous, discrete, or mixed-variable decision-making problems.

Keywords

Decision-making Rough Set Theory Fuzzy Cognitive Maps 

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References

  1. 1.
    Pawlak, Z.: Rough sets. Int. J. of Information and Computer Sciences 11, 341–356 (1982)CrossRefGoogle Scholar
  2. 2.
    Bello, R., Verdegay, J.: Rough sets in the Soft Computing environment. Information Science 212, 1–14 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Pérez, R.B., Garcia, M.M.: Probabilistic approaches to the Rough Set Theory and their applications in decisionmaking. In: Espin, R., Pérez, R.B., Cobo, A., Marx, J., Valdés Olmos, R.A. (eds.) Soft Computing for Business Intelligence. SCI, vol. 537, pp. 67–80. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  4. 4.
    Yao, Y.: Three-way decisions with probabilistic rough sets. Information Science 180, 341–353 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Yao, Y.: Three-way decision: An interpretation of rules in rough set theory. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS (LNAI), vol. 5589, pp. 642–649. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Wong, S., Ziarko, W.: Algorithm for inductive learning. Bulletin of the Polish Academy of Sciences Technical Sciences 34, 271–276 (1986)MathSciNetGoogle Scholar
  7. 7.
    Yao, Y.: The superiority of three-way decisions in probabilistic rough set models. Information Science 180, 1080–1096 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kosko, B.: Fuzzy Cognitive Maps. Int. Journal of Man-Machine Studies 24, 65–75 (1986)CrossRefGoogle Scholar
  9. 9.
    Kosko, B.: Hidden patterns in combined and adaptive knowledge networks. International Journal of Approximate Reasoning 2, 377–393 (1988)CrossRefGoogle Scholar
  10. 10.
    Kosko, B.: Fuzzy Engineering. Prentice-Hall Inc., New York (1997)zbMATHGoogle Scholar
  11. 11.
    Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in Fuzzy cognitive maps. Expert Syst. Appl. 36, 5221–5229 (2009)CrossRefGoogle Scholar
  12. 12.
    Tsadiras, A.K.: Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Science 178, 3880–3894 (2008)CrossRefGoogle Scholar
  13. 13.
    Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. IEEE Transactions on Data and Knowledge Engineering 12, 331–336 (2000)CrossRefGoogle Scholar
  14. 14.
    Filiberto, Y., Bello, R., et al.: A method to build similarity relations into extended rough set theory. In: Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, ISDA 2010, pp. 1314–1319. IEEE (2010)Google Scholar
  15. 15.
    Wilson, D.R., Martínez, T.R.: Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Miao, Y., Liu, Z.Q.: On causal inference in fuzzy cognitive maps. IEEE Transactions on Fuzzy Systems 8, 107–119 (2000)CrossRefGoogle Scholar
  17. 17.
    León, M., Nápoles, G., García, M.M., Bello, R., Vanhoof, K.: Two Steps Individuals Travel Behavior Modeling through Fuzzy Cognitive Maps Pre-definition and Learning. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part II. LNCS (LNAI), vol. 7095, pp. 82–94. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    León, M., Nápoles, G., Bello, R., Mkrtchyan, L., Depaire, B., Vanhoof, K.: Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps. International Journal of Computational Intelligence Systems 6, 1012–1039 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gonzalo Nápoles
    • 1
    • 2
  • Isel Grau
    • 1
  • Koen Vanhoof
    • 2
  • Rafael Bello
    • 1
  1. 1.Universidad Central “Marta Abreu” de Las VillasSanta ClaraCuba
  2. 2.Hasselt UniversityDiepenbeekBelgium

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