DocumentCode :
2238610
Title :
An Extensional Signed Lambda-fuzzy Measure Based on Signed Monotonicity
Author :
Liu, Hsiang-chuan
Author_Institution :
Dept. of Bioinf. & Med. Inf., Asia Univ., Wufeng, Taiwan
fYear :
2010
fDate :
18-20 Nov. 2010
Firstpage :
47
Lastpage :
52
Abstract :
In this paper, first of all, for generalizing the notion of monotonicity, a most important key word, called signed monotonicity, is proposed, it is a generalization of not only the non-negative monotonicity but also the revised monotonicity, furthermore, the signed measure satisfying signed monotonicity, called a generalized signed fuzzy measure, is also proposed. It is proved that this new measure is a generalization of not only the non-negative fuzzy measure but also the signed fuzzy measure, and the well-known signed additive measure is not only a signed fuzzy measure but also a generalized signed fuzzy measure, moreover, the author points out that the signed measure satisfying Lambda-rule , called generalized Lambda-measure is not a signed fuzzy measure and generalized signed fuzzy measure, since it does not satisfy the revised monotonicity and signed monotonicity. At last, a new signed measure satisfying signed monotonicity based on Sugeno´s Lambda-measure, called extensional sign Lambda-fuzzy measure is also proposed, it is a generalized signed fuzzy measure and more useful than above mentioned signed measures based on Lambda-measure for using to analysis multi-criteria decision making problem, since the multi-criteria decision making problem needs to consider the signed monotoniccity. Some related properties are also discussed.
Keywords :
fuzzy set theory; generalisation (artificial intelligence); extensional signed lambda-fuzzy measurement; generalized signed fuzzy measure; multicriteria decision making problem; nonnegative monotonicity; revised monotonicity; signed additive measurement; signed monotonicity notion; Fuzzy measure; revised monotonicity; signed fuzzy measure; signed measure; signed monotonicity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Technologies and Applications of Artificial Intelligence (TAAI), 2010 International Conference on
Conference_Location :
Hsinchu
Print_ISBN :
978-1-4244-8668-7
Electronic_ISBN :
978-0-7695-4253-9
Type :
conf
DOI :
10.1109/TAAI.2010.19
Filename :
5695431
Link To Document :
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