Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches

By Richard Jensen, Qiang Shen. IEEE Transactions on Knowledge and Data Engineering, 2004

696 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/7533254ef22ce048bfa1c6967ddf3588d3bab171

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. Zongxia Xie
5. Qiang Shen
6. Y. Qian
7. Jiye Liang
8. Jinfu Liu

In Information Systems

1. Qinghua Hu
2. Daren Yu
3. Jinfu Liu
4. Richard Jensen
5. Congxin Wu
6. Y. Qian
7. Qiang Shen
8. Zongxia Xie

Citations in the corpus

• Granular Computing and Knowledge Reduction in Formal Contexts (2009, background): “Various approaches have also been developed to perform knowledge reduction and obtain optimal true, certain, and possible decision rules in decision systems [20], [22], [25], [26], [27], [31], [41], [42], [62].”
• Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, methodology): “Results with Ant-Based FRFS. 12.3.”
• New Approaches to Fuzzy-Rough Feature Selection (2009, methodology): “As a result of this, the fuzzy lower and upper approximations are redefined as [12]”
• Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, methodology): “The new Fuzzy-Rough set-based Feature Selection (FRFS2) algorithm [23],[24] that is based on the filter approach is used in this paper.”
• Neighborhood classifiers (2008, methodology): “…spaces and introduced neighborhood relation into rough set methodology, which has shown to be a powerful tool to attribute reduction , feature selection, rule extraction and reasoning with uncertainty (Hu, Yu, & Xie, 2006; Hu, Yu, Xie, & Liu, 2006; Jensen & Shen, 2004; Swiniarski & Skowron, 2003).”
• A rough set approach to feature selection based on ant colony optimization (2010, background): “As suggested by Jensen and Shen (2004), each dataset is test for 20 times.”
• Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, neutral): “[18] [24] [46] [49] .”
• Neighborhood rough set based heterogeneous feature subset selection (2008, methodology): “Now, we compare the proposed algorithm with some classical feature selection techniques: information entropy based feature selection widely used [11], consistency based algorithm [4] and rough set based QUICKREDUCT algorithm [13].”
• Dimensionality reduction based on rough set theory: A review (2009, methodology): “Jensen and Shen described the shortcomings of traditional rough set attribute reduction when applied to datasets with real-valued attribute and they proposed a new method based on fuzzy-rough sets [43].”
• Positive approximation: An accelerator for attribute reduction in rough set theory (2010, background): “Shen and Jenshen generalized the dependency function in classical rough set framework to the fuzzy case and proposed a fuzzy-rough QUICKREDUCT algorithm [13,14,48].”