期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 54, 期 8, 页码 1107-1129出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2013.03.017
关键词
Belief functions; Granular computing; Information tables; Multi-scale decision tables; Probabilistic rough set models; Rough sets
资金
- National Natural Science Foundation of China [61272021, 61075120, 11071284, 61202206, 61173181]
- Zhejiang Provincial Natural Science Foundation of China [LZ12F03002]
- Geographical Modeling and Geocomputation Program under the Focused Investment Scheme of The Chinese University of Hong Kong
Human beings often observe objects or deal with data hierarchically structured at different levels of granulations. In this paper, we study optimal scale selection in multi-scale decision tables from the perspective of granular computation. A multi-scale information table is an attribute-value system in which each object under each attribute is represented by different scales at different levels of granulations having a granular information transformation from a finer to a coarser labelled value. The concept of multi-scale information tables in the context of rough sets is introduced. Lower and upper approximations with reference to different levels of granulations in multi-scale information tables are defined and their properties are examined. Optimal scale selection with various requirements in multi-scale decision tables with the standard rough set model and a dual probabilistic rough set model are discussed respectively. Relationships among different notions of optimal scales in multi-scale decision tables are further analyzed. (C) 2013 Elsevier Inc. All rights reserved.
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