Pawlak Z. Rough sets[J]. Int J of Computer and Information Sciences, 1982, 11: 341-356.
[2]
Pawlak Z. Rough sets: Theoretical aspects of reasoning about data[M]. London: Kluwer Academic Publishers, 1991.
[3]
Pawlak Z, Skowron A. Rough sets and Boolean reasoning[J]. Information Sciences, 2007, 177: 41-73.
[4]
Kryszkiewicz M. Rough set approach to incomplete information systems[J]. Information Sciences, 1998, 112: 39-49.
[5]
Leung Y, Li D Y. Maximal consistent block technique for rule acquisition in incomplete information system[J]. Information Sciences, 2003, 153: 85-106.
[6]
Guan Y Y, Wang H K. Set-valued information systems[J]. Information Sciences, 2006, 176: 2507-2525.
[7]
Guan Y Y, Wang H K, Wang Y, et al. Attribute reduction and optimal decision rules acquisition for continuous valued information systems[J]. Information Sciences, 2009, 179: 2974-2984.
[8]
Du Y, Hu Q H, Zhu P F, et al. Rule learning for classification based on neighborhood covering reduction[J]. Information Sciences, 2011, 181: 5457-5467.
[9]
Hu Q H, Yu D, Xie Z X. Neighborhood classifiers[J]. Expert Systems with Applications, 2008, 34: 866-876.
[10]
Greco S, Matarazzo B, Slowinski R. A new rough set approach to evaluation of bankruptcy risk[C]. Operational Tools in the Management of Financial Risks. Dordrecht: Kluwer Academic Publishers, 1998: 121-136.
[11]
Greco S, Matarazzo B, Slowinski R. Rough sets theory for multicriteria decision analysis[J]. European J of Operational Research, 2001, 129: 1-47.
[12]
Greco S, Matarazzo B, Slowinski R. Rough approximation by dominance relation[J]. Int J of Intelligent Systems, 2002, 17: 153-171.
[13]
Greco S, Matarazzo B, Slowinski R. Rough sets methodology for sorting problems in presence of multiple attributes and criteria [J]. European J of Operational Research, 2002, 138: 247-259.
[14]
Shao M W, Zhang W X. Dominance relation and rules in an incomplete ordered information system[J]. Int J of Intelligent Systems, 2005, 20: 13-27.
[15]
Yang X B, Yang J Y, Wu C, et al. Dominance-based rough set approach and knowledge reduct in incomplete ordered information system [J]. Information Sciences, 2008, 178: 1219-1234.
[16]
Yang X B, Xie J, Song X N, et al. Credible rules in incomplete decision system based on descriptors[J]. Knowledge-Based Systems, 2009, 22: 8-17.
[17]
Qian Y H, Dang C Y, Liang J Y, et al. Set-valued ordered information systems[J]. Information Sciences, 2009, 179: 2809-2832.
[18]
Qian Y H, Liang J Y, Dang C Y. Interval ordered information systems [J]. Computers and Mathematics with Applications, 2008, 56: 1994-2009.
[19]
Yang X B, Yu D J, Yang J T, et al. Dominance-based rough set approach to incomplete interval-valued information system[J]. Data & Knowledge Engineering, 2009, 68(11): 1331-1347.
[20]
Huang B. Graded dominance interval-based fuzzy objective information systems[J]. Knowledge-Based Systems, 2011, 24: 1004-1012.
[21]
Huang B, Li H X, Wei D K. Dominance-based rough set model in intuitionistic fuzzy information systems[J]. Knowledge-Based Systems, 2012, 28: 115-123.
[22]
Huang B, Wei D K, Li H X, et al. Using a rough set model to extract rules in dominance-based interval-valued intuitionistic fuzzy information systems[J]. Information Sciences, 2013, 221: 215-229.
[23]
B?aszczynski J, Greco S, S?owinski R. Multi-criteria classification-A new scheme for application of dominancebased decision rules [J]. European J of Operational Research, 2007, 181: 1030-1044.
[24]
B?aszczynski J, Greco S, S?owinski R. Inductive discovery of laws using monotonic rules [J]. Engineering Applications of Artificial Intelligence, 2012, 25: 284-294.