Rough-fuzzy rule interpolation

Chengyuan Chen, Neil MacParthalain, Ying Li, Christopher Price, Chai Quek, Qiang Shen

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

43 Dyfyniadau(SciVal)
177 Wedi eu Llwytho i Lawr (Pure)


Fuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a useful conclusion. Unfortunately, very little of the existing work on fuzzy rule interpolation can conjunctively handle more than one form of uncertainty in the rules or observations. In particular, the difficulty in defining the required precise-valued membership functions for the fuzzy sets that are used in conventional fuzzy rule interpolation techniques significantly restricts their application. In this paper, a novel rough-fuzzy approach is proposed in an attempt to address such difficulties. The proposed approach allows the representation, handling and utilisation of different levels of uncertainty in knowledge. This allows transformation-based fuzzy rule interpolation techniques to model and harness additional uncertain information in order to implement an effective fuzzy interpolative reasoning system. Final conclusions are derived by performing rough-fuzzy interpolation over this representation. The effectiveness of the approach is illustrated by a practical application to the prediction of diarrhoeal disease rates in remote villages. It is further evaluated against a range of other benchmark case studies. The experimental results confirm the efficacy of the proposed work
Iaith wreiddiolSaesneg
Tudalennau (o-i)1-17
Nifer y tudalennau17
CyfnodolynInformation Sciences
Dyddiad ar-lein cynnar27 Chwef 2016
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 10 Gorff 2016

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