[1]QIAN Jin,TONG Zhigang,YU Ying,et al.Multi-source information fusion through generalized adaptive multi-granulation[J].CAAI Transactions on Intelligent Systems,2023,18(1):173-185.[doi:10.11992/tis.202208030]
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Multi-source information fusion through generalized adaptive multi-granulation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 1 Page number: 173-185 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2023-01-05
Title:
Multi-source information fusion through generalized adaptive multi-granulation
Author(s):
QIAN Jin1;  TONG Zhigang1;  YU Ying1;  HONG Chengxin1;  MIAO Duoqian1; 2
1. College of Software Engineering, East China Jiaotong University, Nanchang 330013, China;
2. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
Keywords:
multi-source information; information fusion; decision-theoretic rough set; rough set; generalized adaptive multi-granulation; multi-granulation; adaptive thresholds; knowledge granularity
CLC:
TP391
DOI:
10.11992/tis.202208030
Abstract:
The multi-granulation rough set model is an effective information fusion strategy. In this paper, this strategy is used to fuse multi-source information from multiple perspectives, and then this information is translated into a consistent information representation. However, most existing multi-granulation information fusion methods use the same threshold value for each knowledge granularity. As we all know, the origin and noise differ among information sources, and the threshold values of the corresponding knowledge granularity should differ. To this end, in this paper, a generalized adaptive multi-granulation rough set model is proposed by combining a single-parameter decision-theoretic rough set with a generalized multi-granulation rough set. Then, four types of generalized multi-granulation models are designed based on typical fusion strategies so that all models can obtain threshold pairs corresponding to knowledge granularity by setting a compensation coefficient $ \zeta $. Furthermore, the relevant properties of these models are discussed. Finally, the experimental results demonstrate that the proposed model is more flexible and reasonable in practical applications.
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