|
|
|
| Rough Set Model Based on Characteristic Relations in Incomplete Information System |
| YANG XiBei1, YANG JingYu1, WU Chen1, 2, YU DongJun1 |
1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094
2.College of Information Science and Technology, Drexel University, Philadelphia PA 19104, USA |
|
|
|
|
Abstract In this paper, the generalized incomplete information system is taken into consideration, in which unknown values are considered as not only absent but also missing. In this kind of generalized incomplete information system, the characteristic relation is deeply investigated. The classification analysis based on the characteristic relation is discussed due to the two possible unreasonable situations. Therefore, two kinds of new characteristic relations, named as type Ⅰand type Ⅱ, are proposed respectively. Then, characteristic relation of type Ⅲ is constructed by combining the advantages of the previous two new characteristic relations. Finally, an illustrative example is analyzed to indicate the validity of the three new characteristic relations when dealing with the generalized incomplete information systems.
|
|
Received: 24 April 2006
|
|
|
|
|
|
[1] Pawlak Z. Rough Set Theory and Its Applications to Data Analysis. Cybernetics and Systems, 1998, 29(7): 661688
[2] Pawlak Z. Rough Sets and Intelligent Data Analysis. Information Sciences, 2002, 147(1/2/3/4): 112
[3] Wang Guoyin. Extension of Rough Set under Incomplete Information Systems. Journal of Computer Research and Development, 2002, 39(10): 12381243 (in Chinese)
(王国胤.Rough集理论在不完备信息系统中的扩充.计算机研究与发展, 2002, 39(10): 12381243)
[4] Wu Weizhi, Zhang Wenxiu, Li Huaizu. Knowledge Acquisition in Incomplete Fuzzy Information Systems via the Rough Set Approach. Expert Systems, 2003, 20(5): 280286
[5] Leung Y, Li Deyu. Maximal Consistent Block Technique for Rule Acquisition in Incomplete Information Systems. Information Sciences, 2003, 153(supplement): 85106
[6] Zhu Feng, Wang Feiyue. Some Results on Covering Generalized Rough Sets. Pattern Recognition and Artificial Intelligence, 2002, 15(1): 613 (in Chinese)
(祝 峰,王飞跃.关于覆盖广义粗集的一些基本结果.模式识别与人工智能, 2002, 15(1): 613)
[7] Wu Chen, Yang Xibei. Information Granules in General and Complete Coverings // Proc of the IEEE International Conference on Granular Computing. Beijing, China, 2005, Ⅱ: 675678
[8] GrzymalaBusse J W. On the Unknown Attribute Values in Learning from Examples // Proc of the 6th International Symposium on Methodologies for Intelligent Systems. Charlotte, USA, 1991: 368377
[9] Stefanowski J, Tsoukias A. Incomplete Information Tables and Rough Classification. Computational Intelligence, 2001, 17(3): 545566
[10] GrzymalaBusse J W, Wang A Y. Modified Algorithms LEM1 and LEM2 for Rule Induction from Data with Missing Attribute Values // Proc of the 5th International Workshop on Rough Sets and Soft Computing at the 3rd Joint Conference on Information Sciences. Triangle Park, USA, 1997: 6972
[11] Kryszkiewicz M. Rough Set Approach to Incomplete Information Systems. Information Sciences, 1998, 112(1/2/3/4): 3949
[12] GrzymalaBusse J W. Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction // Peters J F, Skowron A, GrzymalaBusse J W, et al, eds. Lecture Notes in Computer Science. Berlin, Germany: SpringerVerlag, 2004, 3100: 7895
[13] GrzymalaBusse J W. Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation // Proc of the 3rd International Conference on Rough Sets and Current Trends in Computing. Uppsala, Sweden, 2004: 244253 |
|
|
|
2007, Vol. 20 
