Information-preserving hybrid data reduction based on fuzzy-rough techniques
By Qinghua Hu, Daren Yu, Zongxia Xie. Pattern Recognition Letters, 2006
467 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/c842f69573324187006519ea26a228ef108a665c
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In Computational Theory and Mathematics
In Computer Vision and Pattern Recognition
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Citations in the corpus
- Positive approximation: An accelerator for attribute reduction in rough set theory (2010, methodology): “In fact, several authors also have used variants of Shannon’s entropy or mutual information to measure uncertainty in rough set theory and construct heuristic algorithm of attribute reduction in rough set theory [22,55,56].”
- Neighborhood rough set based heterogeneous feature subset selection (2008, methodology): “extended Shannon’s entropy to measure the information quantity in a set of fuzzy sets [9] and applied the proposed measure to calculate the uncertainty in fuzzy approximation spaces and used it to reduce heterogeneous data [10], where numerical attributes induce fuzzy relations and symbolic features”
- Fuzzy probabilistic approximation spaces and their information measures (2006, methodology): “The generalized methods were applied to hybrid data reduction [41], mining stock price [17], vocabulary for information retrieval [18] and fuzzy decision rule extraction [19].”
- Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation (2007, methodology): “extended the entropy to measure the information quantity in fuzzy sets [28] and applied the proposed measure to calculate the uncertainty in fuzzy-rough approximation spaces [29] and reduce hybrid data [30].”
- Attribute reduction in decision-theoretic rough set models (2008, methodology): “Different algorithms, approaches and methodologies have been extensively studied [1,36,2,4,11,13,17,20,29,35,41,51].”
- Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches (2008, background): “Information measures for fuzzy indiscernibility relations are presented in [140] for the computation of feature importance.”