Optimal scale selection for multi-scale decision tables
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AbstractHuman 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.
All Author(s) ListWu WZ, Leung Y
Journal nameInternational Journal of Approximate Reasoning
Volume Number54
Issue Number8
Pages1107 - 1129
LanguagesEnglish-United Kingdom
KeywordsBelief functions; Granular computing; Information tables; Multi-scale decision tables; Probabilistic rough set models; Rough sets
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Last updated on 2020-30-03 at 00:30