Transformation Based Backward Fuzzy Rule Interpolation with Multiple Missing Antecedent Values

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.

[1]  Mauro Birattari,et al.  The local paradigm for modeling and control: from neuro-fuzzy to lazy learning , 2001, Fuzzy Sets Syst..

[2]  Ludmila I. Kuncheva,et al.  "Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting , 2003, IEEE Trans. Fuzzy Syst..

[3]  Shyi-Ming Chen,et al.  Weighted Fuzzy Rule Interpolation Based on GA-Based Weight-Learning Techniques , 2011, IEEE Transactions on Fuzzy Systems.

[4]  Peter Baranyi,et al.  Generalisation of a Rule Interpolation Method Resulting Always in Acceptable Conclusion , 2000 .

[5]  László T. Kóczy,et al.  Fuzzy rule interpolation for multidimensional input spaces with applications: a case study , 2005, IEEE Transactions on Fuzzy Systems.

[6]  Qiang Shen,et al.  Fuzzy Interpolation and Extrapolation: A Practical Approach , 2008, IEEE Transactions on Fuzzy Systems.

[7]  Qiang Shen,et al.  Backward fuzzy interpolation and extrapolation with multiple multi-antecedent rules , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[8]  Qiang Shen,et al.  Fuzzy interpolative reasoning via scale and move transformations , 2006, IEEE Transactions on Fuzzy Systems.

[9]  Abdolreza Mirzaei,et al.  Intrusion detection using fuzzy association rules , 2009, Appl. Soft Comput..

[10]  Ludmil Mikhailov,et al.  An interpretable fuzzy rule-based classification methodology for medical diagnosis , 2009, Artif. Intell. Medicine.

[11]  Szilveszter Kov cs.  Special Issue on Fuzzy Rule Interpolation , 2011 .