The investigation of the Bayesian rough set model

By D. Ślęzak, W. Ziarko. International Journal of Approximate Reasoning, 2005

403 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fc20f1f08a1322e50d68f926ddfa0cd17e1770ca

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

In Computational Theory and Mathematics

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

In Management Science and Operations Research

1. Yiyu Yao
2. Chuangyin Dang
3. Y. Qian
4. Jiye Liang

Citations in the corpus

• MGRS: A multi-granulation rough set (2010, methodology): “In the past 10 years, several extensions of the rough set model have been proposed in terms of various requirements, such as the variable precision rough set (VPRS) model (see [43]), the rough set model based on tolerance relation (see [7–9]), the Bayesian rough set model (see [33]), the fuzzy rough”
• Probabilistic rough set approximations (2008, methodology): “An attempt to use probabilistic information for approximations was suggested by Pawlak, Wong, Ziarko [29].”
• Incomplete Multigranulation Rough Set (2010, methodology): “In the past ten years, under different conditions, several extensions of the rough-set model have been accomplished [26]–[28], which include variable precision rough-set model [60], rough-set model based on tolerance relation [13], [14], [32], [45], [47], Bayesian roughset model [46], fuzzy rough-se”