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Transformation Based Backward Fuzzy Rule Interpolation with Multiple Missing Antecedent Values

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Backward Fuzzy Rule Interpolation

Abstract

S-BFRI Jin S, Diao R, Shen Q (Backward fuzzy interpolation and extrapolation with multiple multi-antecedent rules, pp 1170–1177, 2012 [1]) enables a missing antecedent value to be interpolated in a backward fashion by exploiting the other given antecedents and the consequent. S-BFRI works by performing indirect interpolative reasoning which involves several intertwined fuzzy rules, each with multiple antecedents. However, no existing technique, (including BFRI) considers the case where multiple antecedents are absent.

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References

  1. S. Jin, R. Diao, Q. Shen, Backward fuzzy interpolation and extrapolation with multiple multi-antecedent rules, in Proceedings of IEEE International Conference on Fuzzy Systems (2012), pp. 1170–1177

    Google Scholar 

  2. I. Gadaras, L. Mikhailov, An interpretable fuzzy rule-based classification methodology for medical diagnosis. Artif. Intell. Med. 47(1), 25–41 (2009)

    Google Scholar 

  3. A. Tajbakhsh, M. Rahmati, A. Mirzaei, Intrusion detection using fuzzy association rules. Appl. Soft Comput. 9(2), 462–469 (2009)

    Google Scholar 

  4. K.W. Wong, D. Tikk, T.D. Gedeon, L.T. Kóczy, Fuzzy rule interpolation for multidimensional input spaces with applications: a case study. IEEE Trans. Fuzzy Syst. 13(6), 809–819 (2005)

    Google Scholar 

  5. G. Bontempi, H. Bersini, M. Birattari, The local paradigm for modeling and control: from neuro-fuzzy to lazy learning. Fuzzy Sets Syst. 121(1), 59–72 (2001)

    Google Scholar 

  6. L. Kuncheva, Fuzzy versus nonfuzzy in combining classifiers designed by boosting. IEEE Trans. Fuzzy Syst. 11(6), 729–741 (2003)

    Google Scholar 

  7. S.-M. Chen, Y.-C. Chang, Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans. Fuzzy Syst. 19(4), 729–744 (2011)

    Google Scholar 

  8. Z. Huang, Q. Shen, Fuzzy interpolative reasoning via scale and move transformations. IEEE Trans. Fuzzy Syst. 14(2), 340–359 (2006)

    Google Scholar 

  9. D. Tikk, P. Baranyi, T.D. Gedeon, L. Muresan, Generalization of the rule interpolation method resulting always in acceptable conclusion. Tatra Mt. Math. Publ. 21, 73–91 (2001)

    Google Scholar 

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Correspondence to Shangzhu Jin .

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Jin, S., Shen, Q., Peng, J. (2019). Transformation Based Backward Fuzzy Rule Interpolation with Multiple Missing Antecedent Values. In: Backward Fuzzy Rule Interpolation. Springer, Singapore. https://doi.org/10.1007/978-981-13-1654-8_4

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