A MATHEMATICAL EXTENSION OF ROUGH SET-BASED ISSUES TOWARD UNCERTAIN INFORMATION ANALYSIS

Hiroshi Sakai (), Kohei Hayashi, Michinori Nakata () and Dominik Ślȩzak ()
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Hiroshi Sakai: Department of Basic Science, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804, Japan
Kohei Hayashi: Department of Basic Science, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804, Japan
Michinori Nakata: Faculty of Management and Information Science, Josai International University, Gumyo, Togane, Chiba 283, Japan
Dominik Ślȩzak: Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland;

New Mathematics and Natural Computation (NMNC), 2011, vol. 07, issue 03, 543-570

Abstract: Rough set theory was originally proposed for analyzing data gathered in data tables, often referred to as information systems. The lower and upper approximations introduced within this theory are known as the very useful concepts. The theory as a whole now becomes a recognized foundation for granular computing. This paper investigates the rough set-based issues for analyzing table data with uncertainty. In reality, tables with non-deterministic information are focused on instead of tables with deterministic information, and several mathematical properties are examined. Especially, decision rule generation from tables with non-deterministic information is highlighted. This investigation is also applied to tables with uncertain numerical data. As a result, a new mathematical framework for analyzing tables with uncertain information is formalized.

Keywords: Rough sets; non-deterministic information; incomplete information; uncertainty; information analysis (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1142/S1793005711002062

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