A new method for measuring uncertainty and fuzziness in rough set theory

By Jiye Liang, Kwai-Sang Chin, Chuangyin Dang, R. Yam. International Journal of General Systems, 2002

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/aef50d4fa391a56974b267345dd1ca5c16f1687b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Witold Pedrycz
2. Y. Qian
3. Jiye Liang
4. Yiyu Yao
5. Chuangyin Dang
6. Yan Zhao
7. Dominik Lzak
8. Georg Peters

In Computational Mechanics

1. Jiye Liang
2. Yiyu Yao
3. Ganeshsree Selvachandran
4. Xindong Peng

In Computational Theory and Mathematics

1. Yiyu Yao
2. Y. Qian
3. Witold Pedrycz
4. Jiye Liang
5. Chuangyin Dang
6. Dominik Lzak
7. Georg Peters
8. Pawan Lingras

Citations in the corpus

• Incomplete Multigranulation Rough Set (2010, background): “Rough-set theory, proposed by Pawlak and Skowron [24], [26], has become a well-established mechanism for uncertainty management in a wide variety of applications related to artificial intelligence [4], [11], [12], [17], [43], [44], [58].”
• MGRS: A multi-granulation rough set (2010, background): “Rough set theory, originated by Pawlak [16,17], has become a well-established mechanism for uncertainty management in a wide variety of applications related to artificial intelligence [2,5,6,11,20,23,24,31,32,41].”
• Positive approximation: An accelerator for attribute reduction in rough set theory (2010, background): “defined another information entropy and its conditional entropy to measure the uncertainty of an information system and applied the proposed entropy to reduce redundant features [25,26].”