Open AccessAttribute reduction in interval‐valued fuzzy ordered decision tables via evidence theory
Author(s) -
Zhang Jia,
Zhang Xiaoyan,
Xu Weihua
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8312
Subject(s) - rough set , fuzzy set , fuzzy logic , mathematics , interval (graph theory) , reduction (mathematics) , computer science , membership function , type 2 fuzzy sets and systems , argument (complex analysis) , data mining , artificial intelligence , combinatorics , biochemistry , chemistry , geometry
There are two different theory methods that are rough set theory and evidence theory, but these two theories can both handle some incomplete and uncertain information. In this study, these two models are combined in the interval‐valued fuzzy ordered information system (IVFOIS). Belief functions and plausibility functions are proposed based on dominance relations in IVFOISs. The belief and plausibility reducts are defined in interval‐valued fuzzy ordered decision tables (IVFODTs) and the attribute reduction of IVFODTs based on evidence theory is established. Finally, the authors use an instance to verify the above argument.