Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26776
Title: A new approach to fuzzy rule generation : fuzzy extension matrix
Authors: Wang, XZ
Wang, YD
Xu, XF
Ling, WD
Yeung, DS
Keywords: Extension matrix
Fuzzy entropy
Heuristic algorithm
Knowledge acquisition
Learning
Learning from fuzzy examples
Issue Date: 2001
Source: Fuzzy sets and systems, 2001, v. 123, no. 3, p. 291-306 How to cite?
Journal: Fuzzy Sets and Systems 
Abstract: This paper proposes a new approach to fuzzy rule generation from a set of examples with fuzzy representation. The new approach called fuzzy extension matrix incorporates the fuzzy entropy to search for paths and generalizes the concept of crisp extension matrix. By discussing paths of the fuzzy extension matrix, a new heuristic algorithm for generating fuzzy rules is introduced. Compared with the crisp extension matrix, the proposed method has the capability of handling fuzzy representation and tolerating noisy data or missing data. A case study shows that the proposed heuristic algorithm partially inherits the advantages from the crisp case such as simplicity of rules and high learning accuracy. The proposed approach offers a new, practical way to automatically acquire imprecise knowledge.
URI: http://hdl.handle.net/10397/26776
ISSN: 0165-0114
DOI: 10.1016/S0165-0114(01)00002-1
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

41
Last Week
0
Last month
0
Citations as of Jul 8, 2018

WEB OF SCIENCETM
Citations

39
Last Week
0
Last month
0
Citations as of Jul 10, 2018

Page view(s)

72
Last Week
2
Last month
Citations as of Jul 15, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.