The Information Entropy, Rough Entropy And Knowledge Granulation In Rough Set Theory

By Jiye Liang, Zhongzhi Shi. Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2004

341 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0a5cbc8982ede87d9c23c54fb1281e34886c0636

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. Yan Zhao
2. Yiyu Yao
3. Jiye Liang
4. Zhongzhi Shi
5. Deyu Li
6. Dominik Lzak
7. Georg Peters
8. Pawan Lingras

In Computational Mechanics

1. Yiyu Yao
2. Yan Zhao
3. Jiye Liang

In Computational Theory and Mathematics

1. Yan Zhao
2. Yiyu Yao
3. Jiye Liang
4. Deyu Li
5. Dominik Lzak
6. Georg Peters
7. Pawan Lingras
8. Y. Qian

Citations in the corpus

• Information entropy, rough entropy and knowledge granulation in incomplete information systems (2006, background): “Furthermore, the relationships among information entropy, rough entropy and knowledge granulation in complete information systems have been established by Liang and Shi (2004) and Liang and Li (2005).”
• Attribute reduction in decision-theoretic rough set models (2008, background): “For example, Shannon’s entropy and many of its variations have been explored to measure the uncertainty in rough set theory [3,7,15,19,28,37,39], and thus can be understood as different forms of the f measure.”