JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (1): 41-50.doi: 10.6040/j.issn.1671-9352.1.2019.105

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Knowledge acquisition of multi-source data based on multigranularity

WAN Qing1,2*, MA Ying-cang1, WEI Ling2,3   

  1. 1. School of Science, Xian Polytechnic University, Xian 710048, Shaanxi, China;
    2. Institute of Concepts, Cognition and Intelligence, Northwest University, Xian 710127, Shaanxi, China;
    3. School of Mathematics, Northwest University, Xian 710127, Shaanxi, China
  • Published:2020-01-10

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Abstract: Multigranularity cognition is the common strategy for analyzing complex data. Multi-source data is one type of the complex data, and its knowledge acquisition become more complicated because of its multisource. Inspired by the idea of multigranularity, the multi-source attribute reduction is defined based on the pessimistic decision-making strategy in multi-source information systems. The relationships between the multi-source attribute reduction and the attribute reduction are discussed in detail, and the corresponding judgment method of attribute characteristics are given. Finally, the definition of multi-source decision rule is proposed based on the optimistic decision-making strategy in multi-source decision information systems. On the basis of multi-granularity model, the proposed method gives a new perspective of multi-source data analysis, which enriches the study of knowledge acquisition.

Key words: multigranularity, multi-source information system, multi-source attribute reduction, multi-source decision rule

CLC Number: 

  • TP18
[1] YAGER R R. A framework for multi-source data fusion[J]. Information Sciences, 2004, 163(1/2/3): 175-200.
[2] KHAN M A, BANERJEE M. Formal reasoning with rough sets in multiple-source approximation systems[J]. International Journal of Approximate Reasoning, 2008, 49(2):466-477.
[3] LIN Guoping, LIANG Jiye, QIAN Yuhua. An information fusion approach by combining multigranulation rough sets and evidence theory[J]. Information Sciences, 2015, 314:184-199.
[4] 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.
[5] 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.
[6] WEI Wei, LIANG Jiye. Information fusion in rough set theory: an overview[J]. Information Fusion, 2019, 48:107-118.
[7] WU Weizhi, LEUNG Yee. Theory and applications of granular labelled partitions in multi-scale decision tables [J]. Information Sciences, 2011, 181(18):3878-3897.
[8] GANTER B, WILLE R. Formal concept analysis: mathematical fundations[M]. New York: Springer-Verlag, 1999.
[9] 曾望林, 折延宏. 面向对象的多粒度形式概念分析[J]. 计算机科学, 2018,45(10):58-60. ZENG Wanglin, SHE Yanhong. Object-oriented multigranulation formal concept analysis[J]. Computer Science, 2018, 45(10):58-60.
[10] 李金海, 吴伟志, 邓硕. 形式概念分析的多粒度标记理论[J]. 山东大学学报(理学版), 2019, 54(2):30-40. LI Jinhai, WU Weizhi, DENG Shuo. Multi-scale theory in formal concept analysis[J]. Journal of Shandong University(Natural Science), 2019, 54(2): 30-40.
[11] 吴伟志, 陈颖, 徐优红, 等. 协调的不完备多粒度标记决策系统的最优粒度选择[J]. 模式识别与人工智能, 2016, 29(2):108-115. WU Weizhi, CHEN Ying, XU Youhong, et al. Optimal granularity selections in consistent incomplete multi-granular labeled decision systems[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(2):108-115.
[12] WU Weizhi, QIAN Yuhua, LI Tongjun, et al. On rule acquisition in incomplete multi-scale decision tables[J]. Information Sciences, 2017, 378:282-302.
[13] SHE Yanhong, LI Jinhai, YANG Hailong. A local approach to rule acquisition in multi-scale decision tables[J]. Knowledge-Based Systems, 2015, 89:398-410.
[14] HAO Chen, LI Jinhai, FAN Min, et al. Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions[J]. Information Sciences, 2017, 415:213-232.
[15] LI Feng, HU Baoqing. A new approach of optimal scale selection to multi-scale decision tables[J]. Information Sciences, 2017, 381:193-208.
[16] LI Feng, HU Baoqing, WANG Jun. Stepwise optimal scale selection for multi-scale decision tables via attribute significance[J]. Knowledge-Based Systems, 2017, 129:4-16.
[17] 顾沈明, 陆瑾璐, 吴伟志, 等. 广义多尺度决策系统的局部最优粒度选择[J]. 山东大学学报(理学版), 2018, 53(8):4-11. GU Shenming, LU Jinlu, WU Weizhi, et al. Local optimal granularity selections in generalized multi-scale decision systems[J]. Journal of Shandong University(Natural Science), 2018, 53(8):4-11.
[18] XIE Junpin, YANG Minhua, LI Jinhai, et al. Rule acquisition and optimal scale selection in multi-scale formal decision contexts and their applications to smart city[J]. Future Generation Computer Systems, 2018, 83:564-581.
[19] QIAN Yuhua, LIANG Jiye. Rough set method based on multi-granulations[C] // Proceeding of 5th IEEE Conference on Cognitive Informatics. Beijing: IEEE Computer Society, 2006: 297-304.
[20] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS: a multi-granulation rough set[J]. Information Sciences, 2010, 180(6):949-970.
[21] 王丽娟, 杨习贝, 杨静宇, 等. 一种新的不完备多粒度粗糙集[J]. 南京大学学报(自然科学), 2012, 48(4):436-444. WANG Lijuan, YANG Xibei, YANG Jingyu, et al. A new incomplete multigranulation rough sets[J]. Journal of Nanjing University(Natural Science), 2012, 48(4):436-444.
[22] LIU Caihui, MIAO Duoqian, QIAN Jin. On multi-granulation covering rough sets[J]. International Journal of Approximate Reasoning, 2014, 55(6):1404-1418.
[23] HUANG Bing, GUO Chunxiang, ZHUANG Yuliang, et al. Intuitionistic fuzzy multigranulation rough sets[J]. Information Sciences, 2014, 277:299-320.
[24] ZHANG Xiaohong, MIAO Duoqian, LIU Caihui, et al. Constructive methods of rough approximation operators and multigranulation rough sets[J]. Knowledge-Based Systems, 2016, 91:114-125.
[25] 张文修, 仇国芳. 基于粗糙集的不确决策[M]. 北京, 清华大学出版社, 2005. ZHANG Wenxiu, QIU Guofang. Uncertain decision making based on rough sets[M]. Beijing: Tsinghua University Press, 2005.
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