期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 55, 期 1, 页码 225-237出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2013.03.004
关键词
Decision-theoretic rough sets; Granular computing; Multigranulation; Bayesian decision theory
资金
- City University of Hong Kong [SRG: 7002681]
- National Natural Science Fund of China [71031006, 61202018]
- National Key Basic Research and Development Program of China(973) [2013CB329404]
- Program for New Century Excellent Talents in University
- Research Fund for the Doctoral Program of Higher Education [20121401110013]
- Shanxi Scholarship Council of China [201008]
- Program for the Innovative Talents of Higher Learning Institutions of Shanxi, China [20120301]
The Bayesian decision-theoretic rough sets propose a framework for studying rough set approximations using probabilistic theory, which can interprete the parameters from existing forms of probabilistic approaches to rough sets. Exploring rough sets in the viewpoint of multigranulation is becoming one of desirable directions in rough set theory, in which lower/upper approximations are approximated by granular structures induced by multiple binary relations. Through combining these two ideas, the objective of this study is to develop a new multigranulation rough set model, called a multigranulation decision-theoretic rough set. Many existing multigranulation rough set models can be derived from the multigranulation decision-theoretic rough set framework. (C) 2013 Elsevier Inc. All rights reserved.
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