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[15]CHEN Hongmei,LI Tianrui,LUO Chuan,et al.A de- 作者简介： cision-theoretic rough set approach for dynamic data min- 徐健锋，男，1973年生，副教授， ing[J].IEEE transactions on fuzzy systems,2015,23(6) 博土研究生，计算机学会会员，主要研 1958-1970. 究方向为数据挖掘、粗糙集、机器学 [16]LIANG Decui,XU Zeshui,LIU Dun.Three-way de- 习。主持国家自然基金项目1项，参 cisions based on decision-theoretic rough sets with dual 与国家自然科学基金项目2项。 hesitant fuzzy information[J.Information sciences,2017, 396:127-143 [17]ZHANG Hongying,YANG Shuyun,MA Jianmin.Rank- ing interval sets based on inclusion measures and applica- 何宇凡，男.1994年生，硕士研究 tions to three-way decisions[J].Knowledge-based sys- 生，主要研究方向为三支决策、粗糙 tems.2016,91:62-70. 集、粒计算、机器学习。 [18]ZHAO Xuerong,HU Baoqing.Fuzzy probabilistic rough sets and their corresponding three-way decisions[J]. Knowledge-based systems,2016,91:126-142. [19]AZAM N,ZHANG Yan,YAO Jingtao.Evaluation func- tions and decision conditions of three-way decisions with 汤涛，男，1993年生，硕士研究 game-theoretic rough sets[J].European journal of opera- 生，主要研究方向为粗糙集、粒计算、 tional research,2017,261(2):704-714. 机器学习。 [20]DENG Xiaofei,YAO Yiyu.A multifaceted analysis of probabilistic three-way decisions[J].Fundamenta inform- aticae,.2014,132(3):291-313. [21]ZHANG Yan,YAO Jingtao.Gini objective functions forZHOU Bing, YAO Yiyu, LUO Jigang. Cost-sensitive three-way email spam filtering[J]. Journal of intelligent in￾formation systems, 2014, 42(1): 19–45. [7] KHAN M T, AZAM N, KHALID S, et al. A three-way ap￾proach for learning rules in automatic knowledge-based topic models[J]. International journal of approximate reas￾oning, 2017, 82: 210–226. [8] LI Huaxiong, ZHANG Libo, ZHOU Xianzhong, et al. Cost-sensitive sequential three-way decision modeling us￾ing a deep neural network[J]. International journal of ap￾proximate reasoning, 2017, 85: 68–78. [9] QIAN Jin, DANG Chuangyin, YUE Xiaodong, et al. At￾tribute reduction for sequential three-way decisions under dynamic granulation[J]. International journal of approx￾imate reasoning, 2017, 85: 196–216. [10] YU Hong, ZHANG Cong, WANG Guoyin. A tree-based incremental overlapping clustering method using the three-way decision theory[J]. Knowledge-based systems, 2016, 91: 189–203. [11] LUO Chuan, LI Tianrui, CHEN Hongmei, et al. Efficient updating of probabilistic approximations with increment￾al objects[J]. Knowledge-based systems, 2016, 109: 71–83. [12] NAUMAN M, AZAM N, YAO Jingtao. A three-way de￾cision making approach to malware analysis using prob￾abilistic rough sets[M]. New York, NY, USA: Elsevier, 2016. [13] CHEN Yufei, YUE Xiaodong, FUJITA H, et al. Three￾way decision support for diagnosis on focal liver lesions[J]. Knowledge-based systems, 2017, 127: 85–99. [14] CHEN Hongmei, LI Tianrui, LUO Chuan, et al. A de￾cision-theoretic rough set approach for dynamic data min￾ing[J]. IEEE transactions on fuzzy systems, 2015, 23(6): 1958–1970. [15] LIANG Decui, XU Zeshui, LIU Dun. Three-way de￾cisions based on decision-theoretic rough sets with dual hesitant fuzzy information[J]. Information sciences, 2017, 396: 127–143. [16] ZHANG Hongying, YANG Shuyun, MA Jianmin. Rank￾ing interval sets based on inclusion measures and applica￾tions to three-way decisions[J]. Knowledge-based sys￾tems, 2016, 91: 62–70. [17] ZHAO Xuerong, HU Baoqing. Fuzzy probabilistic rough sets and their corresponding three-way decisions[J]. Knowledge-based systems, 2016, 91: 126–142. [18] AZAM N, ZHANG Yan, YAO Jingtao. Evaluation func￾tions and decision conditions of three-way decisions with game-theoretic rough sets[J]. European journal of opera￾tional research, 2017, 261(2): 704–714. [19] DENG Xiaofei, YAO Yiyu. A multifaceted analysis of probabilistic three-way decisions[J]. Fundamenta inform￾aticae, 2014, 132(3): 291–313. [20] [21] ZHANG Yan, YAO Jingtao. Gini objective functions for three-way classifications[J]. International journal of ap￾proximate reasoning, 2017, 81: 103–114. GRECO S, MATARAZZO B, ROMAN S. Rough sets theory for multicriteria decision analysis[J]. European journal of operational research, 2001, 129(1): 1–47. [22] LIU Dun, LI Tianrui, ZHANG Junbo. Incremental updat￾ing approximations in probabilistic rough sets under the variation of attributes[J]. Knowledge-based systems, 2015, 73: 81–96. [23] LI Shaoyong, LI Tianrui, LIU Dun. Incremental updating approximations in dominance-based rough sets approach under the variation of the attribute set[J]. Knowledge￾based systems, 2013, 40: 17–26. [24] ZHANG Junbo, LI Tianrui, RUAN Da, et al. Neighbor￾hood rough sets for dynamic data mining[J]. International journal of intelligent systems, 2012, 27(4): 317–342. [25] LUO Chuan, LI Tianrui, CHEN Hongmei. Dynamic maintenance of three-way decision rules[C]//Proceedings of the 9th International Conference on Rough Sets and Knowledge Technology. Shanghai, China, 2014: 801– 811. [26] PAPANDREOU G, KOKKINOS I, SAVALLE P A. Modeling local and global deformations in Deep Learn￾ing: epitomic convolution, Multiple Instance Learning, and sliding window detection[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recog￾nition. Boston, MA, USA, 2015: 390–399. [27] YUN U, LEE G. Sliding window based weighted eras￾able stream pattern mining for stream data applications[J]. Future generation computer systems, 2016, 59: 1–20. [28] 作者简介： 徐健锋，男，1973 年生，副教授， 博士研究生，计算机学会会员，主要研 究方向为数据挖掘、粗糙集、机器学 习。主持国家自然基金项目 1 项，参 与国家自然科学基金项目 2 项。 何宇凡，男，1994 年生， 硕士研究 生，主要研究方向为三支决策、粗糙 集、粒计算、机器学习。 汤涛，男，1993 年生，硕士研究 生，主要研究方向为粗糙集、粒计算、 机器学习。 ·750· 智 能 系 统 学 报 第 13 卷