TY - JOUR
T1 - Rough-fuzzy rule interpolation
AU - Chen, Chengyuan
AU - Parthaláin, Neil Mac
AU - Li, Ying
AU - Price, Chris
AU - Quek, Chai
AU - Shen, Qiang
N1 - Publisher Copyright:
© 2016 The Authors. Published by Elsevier Inc.
PY - 2016/7/10
Y1 - 2016/7/10
N2 - 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.
AB - 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.
KW - Fuzzy rule interpolation
KW - Rough-fuzzy sets
KW - Transformation-based interpolation
UR - http://www.scopus.com/inward/record.url?scp=84960921021&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2016.02.036
DO - 10.1016/j.ins.2016.02.036
M3 - 文章
AN - SCOPUS:84960921021
SN - 0020-0255
VL - 351
SP - 1
EP - 17
JO - Information Sciences
JF - Information Sciences
ER -