A learning procedure to estimate missing values in fuzzy preference relations based on additive consistency
作者： Sergio Alonso , Francisco Chiclana , Francisco Herrera , Enrique Herrera-Viedma
DOI: 10.1007/978-3-540-27774-3_22
关键词: Missing data 、 Complete information 、 Preference 、 Property (philosophy) 、 Consistency (statistics) 、 Measure (mathematics) 、 Mathematics 、 Decision support system 、 Fuzzy logic 、 Data mining
摘要: In decision-making, information is usually provided by means of fuzzy preference relations. However, there may be cases in which experts do not have an in-depth knowledge the problem to solved, and thus their relations incomplete, i.e. some values given or missing. this paper we present a procedure find out missing incomplete relation using known. We also define expert consistency measure, based on additive property. show that our maintains original, expert. Finally, illustrate all this, example presented.
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