Title :
An Efficient Elimination of Input Data in the OWA Aggregation
Author :
Ahn, Byeong Seok
Author_Institution :
ChungAng Univ., Seoul
Abstract :
In the paper, we present an efficient method for pruning multiple alternatives in the OWA aggregation. The proposed method intends to identify inferior alternatives per se and diminish the number of alternatives without any efforts to exploit the OWA operator weights from decision maker. The efficacy of the proposed method is verified by simulation analysis in which different levels of alternatives and different levels of criteria are used.
Keywords :
data analysis; set theory; data filtering; data screening; decision making; finite sets; input data elimination; ordered weighted averaging operator; simulation analysis; Aggregates; Analytical models; Decision making; Educational institutions; Filtering; Linear matrix inequalities; Open wireless architecture; System testing; Uncertainty; Vectors;
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
DOI :
10.1109/GrC.2007.93
