Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 6, Issue 6, Pages 1005-1018Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-015-0407-9
Keywords
Decision-theoretic rough sets; Multigranulation; Bayesian decision procedure; Multigranulation decision-theoretic rough sets; Incomplete information systems
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Funding
- National Natural Science Foundation of China [61473181]
- China Postdoctoral Science Foundation funded project [2013M532063]
- Shaanxi Province Postdoctoral Science Foundation funded project
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We study multigranulation decision-theoretic rough sets in incomplete information systems. Based on Bayesian decision procedure, we propose the notions of weighted mean multigranulation decision-theoretic rough sets, optimistic multigranulation decision-theoretic rough sets, and pessimistic multigranulation decision-theoretic rough sets in an incomplete information system. We investigate the relationships between the proposed multigranulation decision-theoretic rough set models and other related rough set models. We also study some basic properties of these models. We give an example to illustrate the application of the proposed models.
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