Induction of accurate and interpretable fuzzy rules from preliminary crisp representation

Tianhua Chen, Changjing Shang, Pan Su, Qiang Shen

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

53 Dyfyniadau (Scopus)
159 Wedi eu Llwytho i Lawr (Pure)


This paper proposes a novel approach for building transparent knowledge-based systems by generating accurate and interpretable fuzzy rules. The learning mechanism reported here induces fuzzy rules via making use of only predefined fuzzy labels that reflect prescribed notations and domain expertise, thereby ensuring transparency in the knowledge model adopted for problem solving. It works by mapping every coarsely learned crisp production rule in the knowledge base onto a set of potentially useful fuzzy rules, which serves as an initial step towards an intuitive technique for similarity-based rule generalisation. This is followed by a procedure that locally selects a compact subset of the emerging fuzzy rules, so that the resulting subset collectively generalises the underlying original crisp rule. The outcome of this local procedure forms the input to a global genetic search process, which seeks for a trade-off between accuracy and complexity of the eventually induced fuzzy rule base while maintaining transparency. Systematic experimental results are provided to demonstrate that the induced fuzzy knowledge base is of high performance and interpretability.
Iaith wreiddiolSaesneg
Tudalennau (o-i)152-166
Nifer y tudalennau15
CyfnodolynKnowledge-Based Systems
Dyddiad ar-lein cynnar09 Chwef 2018
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 15 Ebr 2018

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