第6期 张晓鹤，等：基于信息嫡的对象加权概念格 ·1103· to knowledge reduction based on variable precision rough [23]LI Junyu,WANG Xia,WU Weizhi,et al.Attribute reduc- set model[J].Information sciences,2004,159(3-4): tion in inconsistent formal decision contexts based on 255-272. congruence relations[J].International journal of machine [13]MI Jusheng,LEUNG Y,WU Weizhi.Approaches to at- learning and cybernetics,2017,8(1):81-94. tribute reduction in concept lattices induced by [24]张继福，张素兰，郑链加权概念格及其渐进式构造) axialities[J].Knowledge-based systems,2010,23(6): 模式识别与人工智能，2005,18(2)：171-176. 504511. ZHANG Jifu,ZHANG Sulan,ZHENG Lian.Weighted [14]CHEN Jinkun,LI Jinjin.An application of rough sets to concept lattice and incremental construction[J].Pattern re- graph theory[J].Information sciences,2012,201: cognition and artificial intelligence,2005,18(2): 114-127. 171-176. [15]CHEN Jinkun,MI Jusheng,XIE Bin,et al.A fast attrib- [25]张素兰，郭平，张继福.基于信息嫡和偏差的加权概念 ute reduction method for large formal decision 格内涵权值获取.北京理工大学学报，2011,31(1)方 contexts[J].International journal of approximate reason- 59-63. ing,2019,106:1-17. ZHANG Sulan,GUO Ping,ZHANG Jifu.Intension [16]SHAO Mingwen,LEUNG Y,WU Weizhi.Rule acquisi- weight value acquisition of weighted concept lattice based tion and complexity reduction in formal decision con- on information entropy and deviance[J].Transactions of texts[J].International journal of approximate reasoning, Beijing Institute of Technology,2011,31(1):59-63. 2014,55(1259-274 作者简介： [17]LI Jinhai,MEI Changlin,LV Yuejin.Knowledge reduc- 张晓鹤，博士研究生，主要研究方 tion in decision formal contexts[J].Knowledge-based sys- 向为概念格、关联规则挖掘。 tems,2011,24(5):709-715. [18]LI Jinhai,MEI Changlin,LV Yuejin.Knowledge reduc- tion in real decision formal contexts[J].Information sci- ences,.2012,189:191-207. [19]LI Jinhai,MEI Changlin,WANG Junhong,et al.Rule- preserved object compression in formal decision contexts 陈德刚，教授，博土生导师，主要 using concept lattices[J].Knowledge-based systems, 研究方向为机器学习、数据挖掘。完 成自然科学基金面上项目3项、数学 2014,71:435-445 天元基金1项，参加973课题1项。 [20]QI Jianjun,WEI Ling,YAO Yiyu.Three-way formal 发表学术论文150余篇。 concept analysis[C]//Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Shanghai:Springer,2014:732-741. [21]REN Ruisi,WEI Ling.The attribute reductions of three- 米据生，教授，博土生导师，主要 研究方向为粗糙集、粒计算、概念格 way concept lattices[J].Knowledge-based systems,2016, 数据挖掘与近似推理。主持国家自然 99:92-102. 科学基金项目3项，教育部博土点基 [22]WEI Ling.LIU Lin,QI Jianjun,et al.Rules acquisition of 金项目1项。获得省级自然科学奖 formal decision contexts based on three-way concept lat- 3项，发表学术论文130余篇。 tices[J].Information sciences,2020,516:529-544.to knowledge reduction based on variable precision rough set model[J]. Information sciences, 2004, 159(3−4): 255–272. MI Jusheng, LEUNG Y, WU Weizhi. Approaches to at￾tribute reduction in concept lattices induced by axialities[J]. Knowledge-based systems, 2010, 23(6): 504–511. [13] CHEN Jinkun, LI Jinjin. An application of rough sets to graph theory[J]. Information sciences, 2012, 201: 114–127. [14] CHEN Jinkun, MI Jusheng, XIE Bin, et al. A fast attrib￾ute reduction method for large formal decision contexts[J]. International journal of approximate reason￾ing, 2019, 106: 1–17. [15] SHAO Mingwen, LEUNG Y, WU Weizhi. Rule acquisi￾tion and complexity reduction in formal decision con￾texts[J]. International journal of approximate reasoning, 2014, 55(1): 259–274. [16] LI Jinhai, MEI Changlin, LV Yuejin. Knowledge reduc￾tion in decision formal contexts[J]. Knowledge-based sys￾tems, 2011, 24(5): 709–715. [17] LI Jinhai, MEI Changlin, LV Yuejin. Knowledge reduc￾tion in real decision formal contexts[J]. Information sci￾ences, 2012, 189: 191–207. [18] LI Jinhai, MEI Changlin, WANG Junhong, et al. Rule￾preserved object compression in formal decision contexts using concept lattices[J]. Knowledge-based systems, 2014, 71: 435–445. [19] QI Jianjun, WEI Ling, YAO Yiyu. Three-way formal concept analysis[C]//Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Shanghai: Springer, 2014: 732−741. [20] REN Ruisi, WEI Ling. The attribute reductions of three￾way concept lattices[J]. Knowledge-based systems, 2016, 99: 92–102. [21] WEI Ling, LIU Lin, QI Jianjun, et al. Rules acquisition of formal decision contexts based on three-way concept lat￾tices[J]. Information sciences, 2020, 516: 529–544. [22] LI Junyu, WANG Xia, WU Weizhi, et al. Attribute reduc￾tion in inconsistent formal decision contexts based on congruence relations[J]. International journal of machine learning and cybernetics, 2017, 8(1): 81–94. [23] 张继福, 张素兰, 郑链. 加权概念格及其渐进式构造 [J]. 模式识别与人工智能, 2005, 18(2): 171–176. ZHANG Jifu, ZHANG Sulan, ZHENG Lian. Weighted concept lattice and incremental construction[J]. Pattern re￾cognition and artificial intelligence, 2005, 18(2): 171–176. [24] 张素兰, 郭平, 张继福. 基于信息熵和偏差的加权概念 格内涵权值获取 [J]. 北京理工大学学报, 2011, 31(1): 59–63. ZHANG Sulan, GUO Ping, ZHANG Jifu. Intension weight value acquisition of weighted concept lattice based on information entropy and deviance[J]. Transactions of Beijing Institute of Technology, 2011, 31(1): 59–63. [25] 作者简介： 张晓鹤，博士研究生，主要研究方 向为概念格、关联规则挖掘。 陈德刚，教授，博士生导师，主要 研究方向为机器学习、数据挖掘。完 成自然科学基金面上项目 3 项、数学 天元基金 1 项，参加 973 课题 1 项。 发表学术论文 150 余篇。 米据生，教授，博士生导师，主要 研究方向为粗糙集、粒计算、概念格、 数据挖掘与近似推理。主持国家自然 科学基金项目 3 项，教育部博士点基 金项目 1 项。获得省级自然科学奖 3 项，发表学术论文 130 余篇。 第 6 期 张晓鹤，等：基于信息熵的对象加权概念格 ·1103·