| [1] VLUYMANS S, CORNELIS C, HRRERA F, et al. Multi-label Classification Using a Fuzzy Rough Neighborhood Consensus. Information Sciences, 2018, 433/434: 96-114.
[2] HUANG K H, LIN H T. Cost-Sensitive Label Embedding for Multi-label Classification. Machine Learning, 2017, 106(9/10): 1725-1746.
[3] KAZAWA H, IZUMITANI T, TAIRA H, et al. Maximal Margin Labeling for Multi-topic Text Categorization // SAUL L K, WEISS Y, BOTTOU L, eds. Advances in Neural Information Processing Systems 17. Cambridge, USA: The MIT Press, 2005: 649-656.
[4] SCHAPIRE R, SINGER Y. Boostexter: A Boosting-Based System for Text Categorization. Machine Learning, 2000, 39( 2/3): 135-168.
[5] ZHANG M L, ZHOU Z H. Multi-label Neural Networks with Applications to Functional Genomics and Text Categorization. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10):1338-1351.
[6] 李 华,李德玉,王素格,等.多标记数据特征提取方法的核改进.计算机应用, 2015, 35(7): 1939-1944, 1954.
(LI H, LI D Y, WANG S G, et al. Kernel Improvement of Multi-label Feature Extraction Method. Journal of Computer Applications, 2015, 35(7): 1939-1944, 1954.)
[7] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970.
[8] QIAN Y H, LIANG J Y, DANG C Y. Incomplete Multigranulation Rough Set. IEEE Transactions on Systems, Man, and Cybernetics(Systems and Humans), 2010, 40(2): 420-431.
[9] YANG X B, SONG X N, CHEN Z H, et al. On Multigranulation Rough Sets in Incomplete Information System. International Journal of Machine Learning and Cybernetics, 2012, 3(3): 223-232.
[10] XU W H, WANG Q R, ZHANG X T. Multi-granulation Rough Sets Based on Tolerance Relations. Soft Computing, 2013, 17(7): 1241-1252.
[11] LIN G P, LIANG J Y, QIAN Y H. Multigranulation Rough Sets: From Partition to Covering. Information Sciences, 2013, 241: 101-118.
[12] TAN A H, WU W Z, LI J J, et al. Evidence-Theory-Based Numerical Characterization of Multigranulation Rough Sets in Incomplete Information Systems. Fuzzy Sets and Systems, 2016, 294:18-35.
[13] YU J H, ZHANG X Y, ZHAO Z H, et al. Uncertainty Measures in Multigranulation with Different Grades Rough Set Based on Dominance Relation. International Journal of Fuzzy System, 2016, 31(2): 1133-1144.
[14] FENG T, FAN H T, MI J S. Uncertainty and Reduction of Variable Precision Multigranulation Fuzzy Rough Sets Based on Three-Way Decisions. International Journal of Approximate Reasoning, 2017, 85: 36-58.
[15] XU W H, LI W T, ZHANG X T. Generalized Multigranulation Rough Sets and Optimal Granularity Selection. Granular Computing, 2017, 2(4): 271-288.
[16] PAWLAK Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Boston, USA: Kluwer Academic Publishers, 1991.
[17] WANG C Z, HE Q, CHEN D G, et al. A Novel Method for Attri-bute Reduction of Covering Decision Systems. Information Sciences, 2014, 254: 181-196.
[18] QIAN J, DANG C Y, YUE X D, et al. Attribute Reduction for Se-
quential Three-Way Decisions under Dynamic Granulation. International Journal of Approximate Reasoning, 2017, 85: 196-216.
[19] WEI W, WU X Y, LIANG J Y, et al. Discernibility Matrix Based Incremental Attribute Reduction for Dynamic Data. Knowledge-Based Systems, 2018, 140: 142-157.
[20] LI H, LI D Y, ZHAI Y H, et al. A Novel Attribute Reduction Approach for Multi-label Data Based on Rough Set Theory. Information Sciences, 2016, 367/368: 827-847.
[21] LIANG M S, MI J S, FENG T. Optimal Granulation Selection for Multi-label Data Based on Multi-granulation Rough Sets. Granular Computing, 2019, 4(3): 323-335.
[22] QIAN Y H, LIANG X Y, LIN G P, et al. Local Multigranulation Decision-Theoretic Rough Sets. International Journal of Approximate Reasoning, 2017, 82: 119-137. |
2019, Vol. 32 
