Paper Info
Title
Improving fuzzy rule interpolation performance with information gain-guided antecedent weighting.
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 significance 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 artificially 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.
Year
DOI
Venue
2018
10.1007/s00500-017-2805-2
Soft Comput.
Keywords
Field
DocType
Fuzzy rule interpolation, Antecedent weighting, Reverse engineering
Data mining,Weighting,Computer science,Interpolation,Artificial intelligence,Computation,Mathematical optimization,Fuzzy rule interpolation,Inference,Information gain,Reverse engineering,Fuzzy logic,Machine learning
Journal
Volume
Issue
ISSN
22
10
1432-7643
Citations 
PageRank 
References 
0
0.34
17
Authors
4
Name
Order
Citations
PageRank
Fangyi Li1246.13
Ying Li213021.36
Changjing Shang321234.92
Qiang Shen4187894.48