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A New Pseudo-metric for Fuzzy Sets

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

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Abstract

A new distance function for fuzzy sets is introduced. It is based on the descriptive complexity, that is, the number of bits (on average) that are needed to describe an element in the symmetric difference of the two sets. The value of the distance gives the amount of additional information needed to describe either one of the two sets when the other is known. We prove that the distance function is a pseudo-metric, namely, it is non-negative, symmetric, it equals zero if the sets are identical and it satisfies the triangle inequality.

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References

  1. Deza, E., Deza, M.: Encyclopedia of Distances. Series in Computer Science, vol. 15. Springer (2009)

    Google Scholar 

  2. Zwick, R., Carlstein, E., Budescu, D.V.: Measures of similarity among fuzzy concepts: A comparative analysis. International Journal of Approximate Reasoning 1, 221–242 (1987)

    Article  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  4. De Baets, B., De Meyer, H., Naessens, H.: A class of rational cardinality-based similarity measures. Journal of Computational and Applied Mathematics 132(1), 51–69 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  5. De Baets, B., Janssens, S., De Meyer, H.: On the transitivity of a parametric family of cardinality-based similarity measures. International Journal of Approximate Reasoning 50(1), 104–116 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  6. Bonissone, P.P.: A pattern recognition approach to the problem of linguistic approximation in system analysis. In: Proceeding of the International Conference on Cybernetics and Society, pp. 793–798 (1979)

    Google Scholar 

  7. Bustince, H., Barrenechea, E., Pagola, M.: Relationship between restricted dissimilarity functions, restricted equivalence functions and normal en-functions: Image thresholding invariant. Pattern Recognition Letters 29(4), 525–536 (2008)

    Article  Google Scholar 

  8. Liu, X.: Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets Syst. 52(3), 305–318 (1992)

    Article  MATH  Google Scholar 

  9. Fan, J., Xie, W.: Some notes on similarity measure and proximity measure. Fuzzy Sets and Systems 101(3), 403–412 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  10. De Luca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control 20(4), 301–312 (1972)

    Article  MATH  MathSciNet  Google Scholar 

  11. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley (1999)

    Google Scholar 

  12. Ratsaby, J.: Information set distance. In: Proceedings of the Mini-Conference on Applied Theoretical Computer Science (MATCOS 2010), Koper, Slovenia, October 13-14, pp. 61–64. University of Primorska Press (2011)

    Google Scholar 

  13. Ratsaby, J.: Combinatorial information distance. In: Enchescu, C., Filip, F.G., Iantovics, B. (eds.) Advanced Computational Technologies, pp. 201–207. Romanian Academy Publishing House (2012)

    Google Scholar 

  14. Ratsaby, J.: Information efficiency. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 475–487. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Chester, U., Ratsaby, J.: Universal distance measure for images. In: Proceedings of the 27th IEEE Convention of Electrical Electronics Engineers in Israel (IEEEI 2012), Eilat, Israel, November 14-17, pp. 1–4 (2012)

    Google Scholar 

  16. Chester, U., Ratsaby, J.: Machine learning for image classification and clustering using a universal distance measure. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds.) SISAP 2013. LNCS, vol. 8199, pp. 59–72. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  17. Couso, I., Garrido, L., Sánchez, L.: Similarity and dissimilarity measures between fuzzy sets: A formal relational study. Information Sciences 229, 122–141 (2013)

    Article  MathSciNet  Google Scholar 

  18. Mitchell, T.: Machine Learning. McGraw Hill (1997)

    Google Scholar 

  19. Giraud-Carrier, C., Martinez, T.: An efficient metric for heterogeneous inductive learning applications in the attribute-value language. In: Yfantis, E.A. (ed.) Intelligent Systems Third Golden West International Conference (Proceedings of GWIC 1994), pp. 341–350. Springer (1995) ISBN 978-0-7923-3422-4

    Google Scholar 

  20. Fisher, D.H.: Knowledge acquisition via incremental conceptual clustering. Machine Learning 2(2), 139–172 (1987)

    Google Scholar 

  21. Gennari, J.H., Langley, P., Fisher, D.: Models of incremental concept formation. Artificial Intelligence 40(1-3), 11–61 (1989)

    Article  Google Scholar 

  22. Cheng, Y., Fu, K.: Conceptual clustering in knowledge organization. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7(5), 592–598 (1985)

    Article  Google Scholar 

  23. Bhatia, S.K., Deogun, J.S.: Conceptual clustering in information retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28(3), 427–436 (1998)

    Article  Google Scholar 

  24. Talavera, L., Bejar, J.: Generality-based conceptual clustering with probabilistic concepts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 196–206 (2001)

    Article  Google Scholar 

  25. Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-Interscience, New York (2006)

    MATH  Google Scholar 

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Kovacs, L., Ratsaby, J. (2014). A New Pseudo-metric for Fuzzy Sets. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-07173-2_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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