by Jiye Liang, Zhongzhi Shi, Deyu Li
Journal of Computational Cognition
http://www.yangsky.com/ijcc124.pdf
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Abstract:
Abstract. Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, applications of inclusion degree in rough set theory are discussed, the relationships among inclusion degree, measures on rough set theory and some generalized rough set models are established. These results will be very helpful for people to understand the essence of rough set theory, and can be regarded as the uniformly theoretical foundation of measures defined in rough set theory. Copyright c○2002 Yang’s Scientific Research Institute, LLC. All rights reserved. 1.
Citations
|
536
|
Rough Sets: Theoretical Aspects of Reasoning about Data
– Pawlak
- 1991
|
|
81
|
Variable Precision Rough Set Model
– Ziarko
- 1993
|
|
47
|
Rough mereology: a new paradigm for approximate reasoning
– Polkowski, Skowron
- 1996
|
|
24
|
Uncertainty measures of rough set prediction
– Düntsch, Gediga
- 1998
|
|
14
|
Rough set approach to incomplete information systems
– Kryszkiewicz
- 1998
|
|
9
|
Data mining using extensions of the rough set model
– Lingras, Yao
- 1998
|
|
8
|
Rough computational methods for information systems
– Guan, Bell
- 1998
|
|
6
|
Computational Methods for Rough Classification and Discovery
– Bell, Guan
- 1998
|
|
3
|
Extraction of Experts’ Decision Rules from Clinical Databases using Rough Set Model
– Tsumoto
- 1998
|
|
3
|
Inclusion degree: a perspective on measures for rough set data analysis, Information Sciences 141
– Xu, Liang, et al.
- 2002
|
|
2
|
Theory of including degrees and its applications to uncertainty inferences, Soft Conputing
– Zhang, Leung
- 1996
|
|
1
|
set theory and its application to data analysis
– Pawlak, Rough
- 1998
|
|
1
|
The algorithm on knowledge reduction in incomplete information systems
– Liang, Xu
- 2002
|
