Show simple item record Li, Fangyi Li, Ying Shang, Changjing Shen, Qiang 2017-08-22T18:31:50Z 2017-08-22T18:31:50Z 2018-05-01
dc.identifier.citation Li , F , Li , Y , Shang , C & Shen , Q 2018 , ' Improving fuzzy rule interpolation performance with information gain-guided antecedent weighting ' Soft Computing , vol. 22 , no. 10 , pp. 3125-3139 . en
dc.identifier.issn 1432-7643
dc.identifier.other PURE: 18866318
dc.identifier.other PURE UUID: 256554ba-7378-4570-8821-abe5091b1a06
dc.identifier.other Scopus: 85028886624
dc.identifier.other 2160/45505
dc.description.abstract Fuzzy rule interpolation (FRI) makes inference possible when dealing with a sparse and imprecise rule base. However, the rule antecedents are commonly assumed to be of equal signicance in most FRI approaches in the implementation of interpolation. This may lead to a poor performance of interpolative reasoning due to inaccurate or incorrect interpolated results. In order to improve the accuracy by minimising the disadvantage of the equal significance assumption, this paper presents a novel inference system where an information gain (IG)-guided fuzzy rule interpolation method is embedded. In particular, the rule antecedents in FRI are weighted using IG to evaluate the relative importance given the consequent for decision making. The computation of antecedent weights is enabled by introducing an innovative reverse engineering process that artifically converts fuzzy rules into training samples. The antecedent weighting scheme is integrated with scale and move transformation-based interpolation (though other FRI techniques may be improved in the same manner). An illustrative example is used to demonstrate the execution of the proposed approach, while systematic comparative experimental studies are reported to demonstrate the potential of the proposed work. en
dc.language.iso eng
dc.relation.ispartof Soft Computing en
dc.rights en
dc.subject fuzzy rule interpolation en
dc.subject antecedent weighting en
dc.subject reverse engineering en
dc.title Improving fuzzy rule interpolation performance with information gain-guided antecedent weighting en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version publishersversion en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution IMPACS en
dc.description.status Peer reviewed en

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