QU Guohua,LI Chunhua,ZHANG Qiang.Attribute reduction and discernibility function in factor space[J].CAAI Transactions on Intelligent Systems,2017,12(06):889-893.[doi:10.11992/tis.201609014]





Attribute reduction and discernibility function in factor space
曲国华1 李春华1 张强2
1. 山西财经大学 管理科学与工程学院, 山西 太原 030006;
2. 北京理工大学 管理与经济学院, 北京 100081
QU Guohua1 LI Chunhua1 ZHANG Qiang2
1. School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China;
2. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
factor spacerough setfactor reductiondiscernibility functionfactorial causality analysis
To enable description, Rough Set theory uses an information system constructed by attributes, and various detailed entropy indexes are employed to achieve the scale of information; this provides a mathematical basis for knowledge mining of relational databases. Current research is focused on the role that Rough Set plays in attribute reduction; however, definition of the discernibility function used for attribute reduction is unclear. For example, when there is no attribute to distinguish between two objects, it is unclear why 1 is used instead of 0 for the corresponding attribute variable. As such, this problem causes a bottleneck when applied in Rough Set. The aim of this paper is to find a more reasonable explanation and application for discernibility functions. The method firstly defines the operation between attribute names, which is different from the operation between attribute values, and the attribute name is different from the attribute value. If operation of the attribute value is confused with that of the attribute name, the meaning will subsequently be unclear. To avoid such confusion, Factor Space theory is employed, as it treats attribute names as factors. The theory uses the operation between factors to define the operation of the attribute name, enabling clear definition of the discernibility function, and explains why the attribute variable takes the value of 1 under special circumstances. Results indicate that Factor Space theory can deepen the theoretical basis of Rough Set and improve its ability to solve problems.


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更新日期/Last Update: 2018-01-03