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| Entropy Based Optimal Scale Reducts for Consistent Generalized Multi-scale Interval-Set Decision Systems |
| HAO Tingting1, WU Weizhi1, TAN Anhui2 |
1. School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022;
2. School of Mathematical Sciences, Huaqiao University, Quan-zhou 362021 |
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Abstract To solve the problem of knowledge acquisition in generalized multi-scale interval-set decision systems, an optimal scale reduction method based on interval-set decision entropy is proposed. First, the concept of scale combinations containing zero scales is defined in the generalized multi-scale interval-set decision systems. Then, similarity relations of the object sets are constructed via conditional attribute sets generated by different scale combinations to obtain the representations of corresponding information granules. Next, with the given scale combination, the concepts of interval lower approximations, interval upper approximations, interval approximation accuracy, and interval roughness of the decision classes with respect to the conditional attribute sets are defined. Furthermore, by integrating conditional entropy with interval roughness,the concept of interval-set decision entropy is introduced along with its properties. Finally, in a consistent generalized multi-scale interval-set decision system, the concepts of optimal scale combination, entropy optimal scale combination and corresponding optimal scale reducts are defined. The equivalence between the two concepts of optimal scale combinations and the equivalence between the concepts of optimal scale reduct and entropy optimal scale reduct are proved. The calculation of an optimal scale reduct is illustrated with an example.
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Received: 07 May 2025
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| Fund:Supported by National Natural Science Foundation of China(No.12371466) |
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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:: HAO Tingting, Master student. Her research interests include rough sets and granular computing.
TAN Anhui, Ph.D., professor. His research interests include granular computing,uncertain data processing and intelligent computing. |
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2025, Vol. 38 
