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| 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 |
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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.
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Received: 15 May 2019
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| Fund:Supported by National Natural Science Foundation of China(No.61573321,61976194,41631179,61773349), Natural Science Foundation of Zhejiang Province(No. LY18F030017) |
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Corresponding Authors:
WU Weizhi, Ph.D., professor. His research interests include rough sets, granular computing, data mining and artificial intelligence.
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| 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 
