Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 54, Issue 8, Pages 1107-1129Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2013.03.017
Keywords
Belief functions; Granular computing; Information tables; Multi-scale decision tables; Probabilistic rough set models; Rough sets
Categories
Funding
- 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
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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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