Fuzzy-Rough Sets Assisted Attribute Selection

By Richard Jensen, Qiang Shen. IEEE transactions on fuzzy systems, 2007

441 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/648c4bd9a09859840ce69c798a6a6e9b84340bd6

How researchers see this paper

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

In Computational Theory and Mathematics

1. Qinghua Hu
2. Daren Yu
3. Richard Jensen
4. Qiang Shen
5. Y. Qian
6. Jiye Liang
7. Chuangyin Dang
8. Jinfu Liu

In Information Systems

1. Richard Jensen
2. Daren Yu
3. Qinghua Hu
4. Qiang Shen
5. Jinfu Liu
6. Qing Xu
7. Jianhua Dai
8. Congxin Wu

In Management Science and Operations Research

1. Qiang Shen
2. Qing Xu
3. Jianhua Dai
4. Richard Jensen
5. Qinghua Hu
6. Daren Yu
7. Y. Qian
8. Chuangyin Dang

Citations in the corpus

• New Approaches to Fuzzy-Rough Feature Selection (2009, methodology): “FRFS has been experimentally evaluated with other leading FS methods (such as Relief-F and entropy-based approaches [12] and [13]) and has been shown to outperform these in terms of resulting classification performance.”
• Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “Results with Ant-Based FRFS. 12.3.”
• 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].”
• Neighborhood rough set based heterogeneous feature subset selection (2008, methodology): “To deal with fuzzy information, Shen and Jensen presented a fuzzy-rough QUICKREDUCT algorithm by generalizing the dependency function defined in the classical rough set model into the fuzzy case [13,14,31].”
• 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].”
• Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification (2013, methodology): “Jensen et al. [23] [24] and Shen et al. [44] generalized the dependency function defined in classical rough set based on positive region into the fuzzy case and presented a rough-fuzzy attribute selection algorithm.”