[1]钱进,童志钢,余鹰,等.基于广义自适应多粒度的多源信息融合研究[J].智能系统学报,2023,18(1):173-185.[doi:10.11992/tis.202208030]
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]
点击复制
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]
基于广义自适应多粒度的多源信息融合研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第1期
页码:
173-185
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2023-01-05
- Title:
- Multi-source information fusion through generalized adaptive multi-granulation
- Keywords:
- multi-source information; information fusion; decision-theoretic rough set; rough set; generalized adaptive multi-granulation; multi-granulation; adaptive thresholds; knowledge granularity
- 分类号:
- TP391
- 摘要:
- 多粒度粗糙集模型是一种有效的信息融合策略。利用该策略能从多个角度将多源信息进行融合，并转化成一致的信息表示。现有的大多数多粒度信息融合方法对每个知识粒度都采用相同的阈值，然而，众所周知，不同的信息源的来源和噪声都不尽相同，其对应的知识粒度的阈值也应不同。为此，首先在广义多粒度粗糙集中引入单参数决策理论粗糙集，提出了广义自适应多粒度粗糙集模型。然后，利用经典的融合策略设计了4种广义多粒度模型，所有模型都可以通过一个参数补偿系数$ \zeta $来自适应地获得知识粒度对应的阈值对，并讨论了这些模型的相关性质。最后，通过实验结果证明，所提模型在实际应用中灵活性更高，决策更为合理。
- 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.
- 参考文献/References:
-
[1] YU Jianhang, XU Weihua. Information fusion for multi-source fuzzy information system with the same structure[C]//2015 International Conference on Machine Learning and Cybernetics. Guangzhou: IEEE, 2015: 170?175.
[2] XU Weihua, YU Jianhang. A novel approach to information fusion in multi-source datasets: a granular computing viewpoint[J]. Information sciences, 2017, 378: 410–423.
[3] SANG Binbin, GUO Yanting, SHI Derong, et al. Decision-theoretic rough set model of multi-source decision systems[J]. International journal of machine learning and cybernetics, 2018, 9(11): 1941–1954.
[4] ZHANG Pengfei, LI Tianrui, YUAN Zhong, et al. A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data[J]. Information fusion, 2022, 80: 87–103.
[5] ZHANG Xiaoyan, CHEN Xiuwei, XU Weihua, et al. Dynamic information fusion in multi-source incomplete interval-valued information system with variation of information sources and attributes[J]. Information sciences, 2022, 608: 1–27.
[6] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS: a multi-granulation rough set[J]. Information sciences, 2010, 180(6): 949–970.
[7] YANG Xibei, QI Yunsong, SONG Xiaoning, et al. Test cost sensitive multigranulation rough set: model and minimal cost selection[J]. Information sciences, 2013, 250: 184–199.
[8] LIU Caihui, MIAO Duoqian, QIAN Jin. On multi-granulation covering rough sets[J]. International journal of approximate reasoning, 2014, 55(6): 1404–1418.
[9] QIAN Yuhua, ZHANG Hu, SANG Yanli, et al. Multigranulation decision-theoretic rough sets[J]. International journal of approximate reasoning, 2014, 55(1): 225–237.
[10] SUN Bingzhen, MA Weimin. Multigranulation rough set theory over two universes[J]. Journal of intelligent & fuzzy systems, 2015, 28(3): 1251–1269.
[11] 梁美社, 米据生, 赵天娜. 广义优势多粒度直觉模糊粗糙集及规则获取[J]. 智能系统学报, 2017, 12(6): 883–888
LIANG Meishe, MI Jusheng, ZHAO Tianna. Generalized dominance-based multi-granularity intuitionistic fuzzy rough set and acquisition of decision rules[J]. CAAI transactions on intelligent systems, 2017, 12(6): 883–888
[12] YU Jianhang, ZHANG Biao, CHEN Minghao, et al. Double-quantitative decision-theoretic approach to multigranulation approximate space[J]. International journal of approximate reasoning, 2018, 98: 236–258.
[13] QIAN Jin, LIU Caihui, MIAO Duoqian, et al. Sequential three-way decisions via multi-granularity[J]. Information sciences, 2020, 507: 606–629.
[14] 李金海, 王飞, 吴伟志, 等. 基于粒计算的多粒度数据分析方法综述[J]. 数据采集与处理, 2021, 36(3): 418–435
LI Jinhai, WANG Fei, WU Weizhi, et al. Review of multi-granularity data analysis methods based on granular computing[J]. Journal of data acquisition and processing, 2021, 36(3): 418–435
[15] ZHANG Pengfei, LI Tianrui, LUO Chuan, et al. AMG-DTRS: adaptive multi-granulation decision-theoretic rough sets[J]. International journal of approximate reasoning, 2022, 140: 7–30.
[16] 林国平, 梁吉业, 钱宇华. 基于多粒度视角下的D-S证据理论融合策略[J]. 计算机科学, 2014, 41(2): 45–48
LIN Guoping, LIANG Jiye, QIAN Yuhua. Multigranulation view based fusing strategy of D-S evidence[J]. Computer science, 2014, 41(2): 45–48
[17] CHE Xiaoya, MI Jusheng, CHEN Degang. Information fusion and numerical characterization of a multi-source information system[J]. Knowledge-based systems, 2018, 145: 121–133.
[18] SANG Binbin, YANG Lei, CHEN Hongmei, et al. Generalized multi-granulation double-quantitative decision-theoretic rough set of multi-source information system[J]. International journal of approximate reasoning, 2019, 115: 157–179.
[19] CHE Xiaoya, MI Jusheng. Attributes set reduction in multigranulation approximation space of a multi-source decision information system[J]. International journal of machine learning and cybernetics, 2019, 10(9): 2297–2311.
[20] 万青, 魏玲, 任睿思. 协调多源决策表的规则提取[J]. 南京大学学报(自然科学), 2020, 56(4): 494–504
WAN Qing, WEI Ling, REN Ruisi. Rule acquisition of consistent multi-source decision tables[J]. Journal of Nanjing University (natural science edition), 2020, 56(4): 494–504
[21] YANG Lei, XU Weihua, ZHANG Xiaoyan, et al. Multi-granulation method for information fusion in multi-source decision information system[J]. International journal of approximate reasoning, 2020, 122: 47–65.
[22] 骆公志, 陈佳馨. 多源覆盖信息系统下的加权广义多粒度粗糙集模型及其应用[J]. 计算机工程与科学, 2021, 43(12): 2231–2237
LUO Gongzhi, CHEN Jiaxin. A weighted generalized multi-granulation rough set model of multi-source covering information system and its applications[J]. Computer engineering & science, 2021, 43(12): 2231–2237
[23] CHU Xiaoli, SUN Bingzhen, CHU Xiaodong, et al. Multi-granularity dominance rough concept attribute reduction over hybrid information systems and its application in clinical decision-making[J]. Information sciences, 2022, 597: 274–299.
[24] PAWLAK Z. Rough sets[J]. International journal of computer & information sciences, 1982, 11(5): 341–356.
[25] YAO Y Y, WONG S K M. A decision theoretic framework for approximating concepts[J]. International journal of man-machine studies, 1992, 37(6): 793–809.
[26] XU Weihua, LI Wentao, ZHANG Xiantao. Generalized multigranulation rough sets and optimal granularity selection[J]. Granular computing, 2017, 2(4): 271–288.
[27] SUO Mingliang, TAO Laifa, ZHU Baolong, et al. Single-parameter decision-theoretic rough set[J]. Information sciences, 2020, 539: 49–80.
- 相似文献/References:
-
[1]马大中,张化光,冯 健,等.一种基于多传感器信息融合的故障诊断方法[J].智能系统学报,2009,4(1):72.
MA Da-zhong,ZHANG Hua-guang,FENG Jian,et al.A fault diagnosis method based on multi-sensor information fusion[J].CAAI Transactions on Intelligent Systems,2009,4():72.
[2]徐立中,林志贵,黄凤辰.基于模糊证据理论的水质环境状态估计[J].智能系统学报,2007,2(3):79.
XU Li-zhong,LIN Zhi-gui,Huang Feng-chen.Water environmental qual ity assessment based on fuzzy evidence theory[J].CAAI Transactions on Intelligent Systems,2007,2():79.
[3]徐立中,等.多源监测信息融合仿生复眼型系统模式及感知计算机理[J].智能系统学报,2008,3(4):328.
XU L i-zhong,SH IAi-ye,et al.A perceptual and computationalmechan ism for bion ic compound eye systems with multi-source information fusion[J].CAAI Transactions on Intelligent Systems,2008,3():328.
[4]黄心汉,李 鹏,王 敏.广义证据推理融合结构[J].智能系统学报,2010,5(6):487.
HUANG Xin-han,LI Peng,WANG Min.General evidence reasoning fusion structure[J].CAAI Transactions on Intelligent Systems,2010,5():487.
[5]夏飞,马茜,张浩,等.改进D-S证据理论在电动汽车锂电池故障诊断中的应用[J].智能系统学报,2017,12(4):526.[doi:10.11992/tis.201605001]
XIA Fei,MA Xi,ZHANG Hao,et al.Application of improved D-S evidence theory in fault diagnosis of lithium batteries in electric vehicles[J].CAAI Transactions on Intelligent Systems,2017,12():526.[doi:10.11992/tis.201605001]
[6]潘家辉,何志鹏,李自娜,等.多模态情绪识别研究综述[J].智能系统学报,2020,15(4):633.[doi:10.11992/tis.202001032]
PAN Jiahui,HE Zhipeng,LI Zina,et al.A review of multimodal emotion recognition[J].CAAI Transactions on Intelligent Systems,2020,15():633.[doi:10.11992/tis.202001032]
[7]陈立潮,解丹,曹建芳,等.改进光流法和GMM融合的车辆实时检测算法研究[J].智能系统学报,2021,16(2):271.[doi:10.11992/tis.201907051]
CHEN Lichao,XIE Dan,CAO Jianfang,et al.Research on vehicle real-time detection algorithm based on improved optical flow method and GMM[J].CAAI Transactions on Intelligent Systems,2021,16():271.[doi:10.11992/tis.201907051]
[8]方涛,陈志国,傅毅.神经网络多层特征信息融合的人脸识别方法[J].智能系统学报,2021,16(2):279.[doi:10.11992/tis.201907053]
FANG Tao,CHEN Zhiguo,FU Yi.Face recognition method based on neural network multi-layer feature information fusion[J].CAAI Transactions on Intelligent Systems,2021,16():279.[doi:10.11992/tis.201907053]
[9]王宇晖,杜军平,邵蓥侠.基于Transformer与技术词信息的知识产权实体识别方法[J].智能系统学报,2023,18(1):186.[doi:10.11992/tis.202203036]
WANG Yuhui,DU Junping,SHAO Yingxia.An intellectual property entity recognition method based on Transformer and technological word information[J].CAAI Transactions on Intelligent Systems,2023,18():186.[doi:10.11992/tis.202203036]
[10]谭立玮,张淑军,韩琪,等.面向医学影像报告生成的门归一化编解码网络[J].智能系统学报,2024,19(2):411.[doi:10.11992/tis.202207013]
TAN Liwei,ZHANG Shujun,HAN Qi,et al.Gate normalized encoder-decoder network for medical image report generation[J].CAAI Transactions on Intelligent Systems,2024,19():411.[doi:10.11992/tis.202207013]
备注/Memo
收稿日期:2022-08-22。
基金项目:国家自然科学基金项目(62066014,62163016);江西省自然科学基金项目(20202BABL202018,20212ACB202001)
作者简介:钱进,教授,博士,主要研究方向为粒计算、大数据挖掘和机器学习。主持国家自然科学基金项目2项、省部级自然科学基金项目3项。获江西省自然科学奖1项。发表学术论文50余篇;童志钢,硕士研究生,主要研究方向为粗糙集、粒计算和大数据挖掘;苗夺谦,教授,博士,国际粗糙集学会理事长、中国人工智能学会会士、中国计算机学会杰出会员,主要研究方向为粒计算、不确定性、大数据分析。荣获中国人工智能学会吴文俊人工智能自然科学二等奖1项;主持国家自然科学基金面上项目7项,发表学术论文180余篇,ESI高被引8篇;出版教材和学术著作10余部。
通讯作者:钱进.E-mail:qjqjlqyf@163.com
更新日期/Last Update:
1900-01-01