Please use this identifier to cite or link to this item:
Title: On the measurement of TL - fuzzy rough sets
Authors: Chen, D
Tsang, ECC
Keywords: Lukasiewicz's triangular norm
Rough sets
Belief function
Fuzzy rough sets
Issue Date: 2006
Publisher: IEEE
Source: 2006 International Conference on Machine Learning and Cybernetics, 13-16 August 2006, Dalian, China, p. 1636-1641 How to cite?
Abstract: In fuzzy rough sets a fuzzy T-similarity relation is employed to describe the degree of similarity between two objects and to construct lower and upper approximations for arbitrary fuzzy sets. Different triangular norm T identifies different point of view of similarity. Thus reasonable selection of triangular norm is clearly meaningful to practical applications of fuzzy rough sets. In this paper we first discuss the selection of triangular norm and emphasize the well-known Lukasiewicz's triangular norm TL as a reasonable selection. We then propose a function for each approximation operator in TL -fuzzy rough sets to measure its approximating ability. The measurement functions of lower and upper approximation operators are natural generalizations of belief and plausibility functions in the evidence theory respectively. By using these two functions, accuracy measure, roughness degree, entropy and conditional entropy are defined for TL-fuzzy rough sets
ISBN: 1-4244-0061-9
DOI: 10.1109/ICMLC.2006.258898
Appears in Collections:Conference Paper

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


Last Week
Last month
Citations as of Feb 8, 2019

Page view(s)

Last Week
Last month
Citations as of Feb 19, 2019

Google ScholarTM



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