NDLI: On the Measurement of TL

Content Provider	IEEE Xplore Digital Library
Author	De-Gang Chen Tsang, E.C.C.
Copyright Year	2006
Description	Author affiliation: Dept. of Math. & Phys., North China Electr. Power Univ., Beijing (De-Gang Chen)
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 $T_{L}$ 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 $T_{L}-fuzzy$ rough sets
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	1636
Ending Page	1641
File Size	221783
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258898
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Rough sets Entropy Fuzzy logic Set theory Fuzzy set theory Mathematics Physics computing Machine learning Cybernetics belief function fuzzy rough sets Lukasiewicz's triangular norm
Content Type	Text
Resource Type	Article

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