中图分类号：
TP13
{{custom_clc.code}}
({{custom_clc.text}})
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Pawlak Z. Rough sets-theoretical aspects of reasoning about data[M]. Dordrecht: Kluwer Academic, 1991.
[2] Park I K, Choi G S. Rough set approach for clustering categorical data using information-theoretic dependency measure[J]. Information Systems, 2015, 48: 289-295.
[3] Hu Q H, Che X J, Zhang L, et al. Rank entropy based decision trees for monotonic classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(11): 2052-2064.
[4] Guo Y G, Jiao L C, Wang S, et al. A novel dynamic rough subspace based selective ensemble[J]. Pattern Recognition, 2015, 48: 1638-1652.
[5] 黄兵, 魏大宽. 基于距离的直觉模糊粗糙集模型及应用[J]. 系统工程理论与实践, 2011, 31(7): 1356-1362.Huang B, Wei D K. Distance-based rough set model in intuitionistic fuzzy information systems and its application[J]. Systems Engineering-Theory & Practice, 2011, 31(7): 1356-1362.
[6] Li H X, Zhang L B, Huang B, et al. Sequential three-way decision and granulation for cost-sensitive face recognition[J]. Knowledge-Based Systems, 2016, 91: 241-251.
[7] 胡清华, 于达任. 应用粗糙计算[M]. 北京: 科学出版社, 2012.Hu Q H, Yu D R. Applied rough computing[M]. Beijing: Science Press, 2012.
[8] Ziarko W. Variable precision rough set model[J]. Journal of Computer and System Science, 1993, 46(1): 39-59.
[9] Greco S, Matarazzo B, Slowinski R. Rough sets theory for multicriteria decision analysis[J]. European Journal of Operational Research, 2002, 129(1): 1-47.
[10] Qian Y H, Zhang H, Sang Y L, et al. Multigranulation decision-theoretic rough sets[J]. International Journal of Approximate Reasoning, 2013, 55: 225-237.
[11] Ju H R, Yang X B, Dou H L, et al. Variable precision multigranulation rough set and attributes reduction[C]//Transactions on Rough Set XVⅢ, Springer, 2014: 52-68.
[12] Ju H R, Yang X B, Qi Y S, et al. Dynamic updating multigranulation fuzzy rough set: Approximations and reducts[J]. International Journal of Machine Learning and Cybernetics, 2014, 5(6): 981-990.
[13] Yang X B, Qi Y, Yu H L, et al. Updating multigranulation rough approximations with increasing of granular structures[J]. Knowledge-Based Systems, 2014, 64: 59-69.
[14] Dubois D, Prade H. Rough fuzzy sets and fuzzy rough sets[J]. International Journal of General Systems, 1990, 17(2-3): 191-209.
[15] Yang X B, Yang J Y. Incomplete information system and rough set theory: Models and attribute reductions[M]. Beijing: Science Press & Springer, 2012.
[16] Zhao Y, Yao Y Y, Luo F. Data analysis based on discernibility and indiscernibility[J]. Information Sciences, 2007, 177(22): 4959-4976.
[17] Palacios A M, Sánchez L, Couso I. Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data[J]. International Journal of Approximate Reasoning, 2011, 52(6): 841-862.
[18] Yang Q, Ling C, Chai X Y, et al. Test-cost sensitive classification on data with missing values[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(5): 626-638.
[19] Zhou Z H, Liu X Y. Training cost-sensitive neural networks with methods addressing the class imbalance problem[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 63-77.
[20] Yao Y Y. The superiority of three-way decisions in probabilistic rough set models[J]. Information Sciences, 2011, 181: 1080-1096.
[21] Jia X Y, Liao W H, Tang Z M, et al. Minimum cost attribute reduction in decision-theoretic rough set models[J]. Information Sciences, 2013, 219: 151-167.
[22] Li W T, Xu W H. Double-quantitative decision-theoretic rough set[J]. Information Sciences, 2015, 316: 54-67.
[23] Ju H R, Yang X B, Yu H L, et al. Cost-sensitive rough set approach[J]. Information Sciences, 2016, 355-356: 282-298.
[24] Min F, He H P, Qian Y H, et al. Test-cost-sensitive attribute reduction[J]. Information Sciences, 2011, 181(22): 4928-4942.
[25] Min F, Zhu W. Attribute reduction of data with error ranges and test costs[J]. Information Sciences, 2012, 211: 48-67.
[26] Min F, Hu Q H, Zhu, W. Feature selection with test cost constraint[J]. International Journal of Approximate Reasoning, 2014, 55(1): 167-179.
[27] 张显勇. 精度与程度逻辑差双量化粗糙集模型的属性约简[J]. 系统工程理论与实践, 2015, 35(11): 2925-2931.Zhang X Y. Attribute reduction for the double-quantitative rough set model based on logical difference of precision and grade[J]. Systems Engineering-Theory & Practice, 2015, 35(11): 2925-2931.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金（61572242，71671086，61473157）；江苏省普通高校研究生科研创新计划项目（KYLX16_0021）
{{custom_fund}}
PDF(751 KB)
