Optimal Scale Selection and Attribute Reduction of Multi-scale Multiset-Valued Information Systems Based on Entropy
WANG Leixi1,2, WU Weizhi1,2, XIE Zhenhuang1,2
1. School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022; 2. Key Laboratory of Oceanograhic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022
Abstract：Existing information systems are difficult to reflect and deal with the data duplication in the process of data fusion. In this paper, the concept of multi-scale multiset-valued information systems is introduced and the optimal scale selection and attribute reduction in these systems are discussed. Firstly, a similarity relation on the universe of discourse from any attribute subset in a multi-scale multiset-valued information system is defined by employing the Hellinger distance on multi-sets of the domain of any attribute. Then, information granules in the form of similarity classes are constructed. Knowledge rough entropy is further introduced in the context of multi-scale multiset-valued information systems. Optimal scales based on the similarity relation and the knowledge rough entropy are defined in a multi-scale multiset-valued information system, respectively. It is examined that the optimal scale based on the similarity relation and entropy optimal scale are equivalent. Finally, reducts and entropy reducts based on the optimal scale are discussed in the multi-scale multiset-valued information system, and algorithms for calculating the entropy optimal scale and an entropy reduct are also designed in a multi-scale multiset-valued information system.
王蕾晰, 吴伟志, 谢祯晃. 基于熵的多尺度多重集值信息系统的最优尺度选择与属性约简[J]. 模式识别与人工智能, 2023, 36(6): 495-510.
WANG Leixi, WU Weizhi, XIE Zhenhuang. Optimal Scale Selection and Attribute Reduction of Multi-scale Multiset-Valued Information Systems Based on Entropy. Pattern Recognition and Artificial Intelligence, 2023, 36(6): 495-510.
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