Variable Precision Based Optimal Scale Combinations in Generalized Multi-scale Decision Systems
NIU Dongran1,2, WU Weizhi1,2, LI Tongjun1,2
1.School of Mathematics, Physics and Information Science, Zhe-jiang Ocean University, Zhoushan 316022; 2.Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022
Abstract To solve the problems of knowledge representation and knowledge acquisition in generalized multi-scale decision systems, optimal scale combination selections based on dual probabilistic rough set model in generalized multi-scale decision systems are discussed. Notions of β lower approximation optimal scale combination, β upper approximation optimal scale combination, β belief distribution optimal scale combination and β plausibility distribution optimal scale combination in generalized multi-scale decision systems are defined and their properties are examined. Finally, relationships among different notions of optimal scale combinations in generalized multi-scale decision systems are analyzed. It is proved that for some special thresholds, β lower approximation optimal scale combination is equivalent to the maximum distribution optimal scale combination, whereas β upper approximation optimal scale combination is equivalent to the generalized decision optimal scale combination.
Fund:Supported by National Natural Science Foundation of China(No.61573321,61976194,41631179,61773349), Natural Science Foundation of Zhejiang Province(No. LY18F030017)
Corresponding Authors:
WU Weizhi, Ph.D., professor. His research interests include rough sets, granular computing, data mining and artificial intelligence.
About author:: NIU Dongran, master student. Her research interests include rough sets and data mining.LI Tongjun, Ph.D., professor. His research interests include granular computing, rough sets, concept lattices and data mining.
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2019, Vol. 32 
