Knowledge Acquisition for Consistent Generalized Decision Multi-scale Ordered InforMation SysteMs
ZHANG Jiaru1,2, WU Weizhi1,2, YANG Ye1,2
1. School of InforMation Engineering, Zhejiang Ocean University, Zhoushan 316022; 2. Key Laboratory of OceanograPhic Big Data Mining and APPlication of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022 decision Multi-scale ordered inforMation systeMs are exPlored.
Abstract A decision Multi-scale inforMation systeM is a sPecial tyPe of Multi-scale data set and each object under each attribute in the systeM froM either the condition attribute set or the decision attribute is rePresented by different scales at different levels of the granulations, holding a granular inforMation transforMation froM a finer to a coarser labelled value. To solve the ProbleM of knowledge acquisition in generalized decision Multi-scale ordered inforMation systeMs, the concePt of scale selection is firstly defined. Each scale selection is linked with a single-scale ordered decision systeM. DoMinance relations are also introduced into decision Multi-scale inforMation systeMs, rePresentations of inforMation granules as well as lower and uPPer aPProxiMations of sets under different scale selections are Presented, and their relationshiPs are exaMined. Then, five tyPes of oPtiMal scale selections in consistent generalized decision Multi-scale ordered inforMation systeMs are defined. It is Proved that there are indeed two different tyPes of oPtiMal scale selections. The notions of oPtiMal scale selection, lower aPProxiMation oPtiMal scale selection and belief oPtiMal scale selection are all equivalent, and a scale selection is uPPer aPProxiMation oPtiMal if and only if it is Plausibly oPtiMal. Finally, based on oPtiMal scale selections, a Method of discernibility Matrix attribute reduction and ordered decision rules hidden in consistent generalized decision Multi-scale ordered inforMation systeMs are exPlored.
Fund:Supported by National Natural Science Foundation of China(No.61976194,62076221)
Corresponding Authors:
WU Weizhi, Ph.D., professor. His research interests include rough set, granular computing, data mining and artificial intelligence.
About author:: ZHANG Jiaru, master student. Her research interests include rough set and granular computing. YANG Ye, master student. Her research interests include rough set and granular computing.
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2022, Vol. 35 
