| [1] PAWLAK Z. Rough Sets: Theoretical Aspect of Reasoning about Data. Dordrecht, The Netherlands: Kluwer Academic Publishers, 1991.
[2] PAWLAK Z. Vagueness and Uncertainty: A Rough Set Perspective. Computational Intelligence, 1995, 11(2): 227-232.
[3] PAWLAK Z, SKOWRON A. Rudiments of Rough Sets. Information Sciences, 2007, 177(1): 3-27.
[4] BAZAN G J. A Comparison of Dynamic Non-dynamic Rough Set Methods for Extracting Laws from Decision Tables // POLKOWSKI L, SKOWRON A, eds. Rough Sets in Knowledge Discovery 1: Methodology and Applications. Heidelberg, Germany: Physica-Verlag, 1998: 321-365.
[5] WANG J, WANG J. Reduction Algorithms Based on Discernibility Matrix: The Order Attributes Method. Journal of Computer Science and Technology, 2001, 16(6): 489-504.
[6] ZHEN Z, WANG G Y, WU Y. A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm // Proc of the 9th International Conference of Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Berlin, Germany: Springer-Verlag, 2003: 122-129.
[7] DENG D Y, WANG J Y, LI X J. Parallel Reducts in a Series of Decision Subsystems // Proc of the 2nd International Joint Confe-rence on Computational Sciences and Optimization. Washington, USA: IEEE, 2009: 377-380.
[8] DENG D Y. Comparison of Parallel Reducts and Dynamic Reducts in Theory. Computer Science, 2009, 36(8A): 176-178.
[9] DENG D Y. Parallel Reducts and Its Properties // Proc of the IEEE International Conference on Granular Computing. Washington, USA: IEEE, 2009: 121-125.
[10] 邓大勇,卢克文,苗夺谦,等.知识系统中全粒度粗糙集及概念漂移的研究.计算机学报, 2019, 42(1): 85-97.
(DENG D Y, LU K W, MIAO D Q, et al. Study on Entire-Granulation Rough Sets and Concept Drifting in a Knowledge System. Chinese Journal of Computers, 2019, 42(1): 85-97)
[11] DENG D Y, YAN D X, CHEN L. Attribute Significance for F-Pa-rallel Reducts // Proc of the IEEE International Conference on Granular Computing. Washington, USA: IEEE, 2011: 156-161.
[12] 邓大勇,姚 坤,肖春水.全粒度粗糙集的不确定性.模式识别与人工智能, 2018, 31(9): 809-815.
(DENG D Y, YAO K, XIAO C S. Uncertainty of Entire-Granulation Rough Sets. Pattern Recognition and Artificial Intelligence, 2018, 31(9): 809-815.)
[13] 邓大勇.全粒度粗糙集属性约简.模式识别与人工智能, 2018, 31(3): 230-235
(DENG D Y. Attribute Reduction for Entire-Granulation Rough Sets. Pattern Recognition and Artificial Intelligence, 2018, 31(3): 230-235.)
[14] 邓大勇,薛欢欢,苗夺谦,等.属性约简准则与约简信息损失的研究.电子学报, 2017, 45(2): 401-407.
(DENG D Y, XUE H H, MIAO D Q, et al. Study on Criteria of Attribute Reduction and Information Loss of Attribute Reduction. Acta Electronica Sinica, 2017, 45(2): 401-407.)
[15] 苗夺谦,胡桂荣.知识约简的一种启发式算法.计算机研究与发展, 1999, 36(6): 681-684.
(MIAO D Q, HU G R. A Heuristic Algorithm for Reduction of Knowledge. Computer Research and Development, 1999, 36(6):681-684.)
[16] 王国胤,于 洪,杨大春.基于条件信息熵的决策表约简.计算机学报, 2002, 25(7): 759-766.
(WANG G Y, YU H, YANG D C. Decision Table Reduction Based on Conditional Information Entropy. Chinese Journal of Computers, 2002, 25(7): 759-766.) |
2019, Vol. 32 
