Neighborhood rough set based heterogeneous feature subset selection
By Qinghua Hu, Daren Yu, Jinfu Liu, Congxin Wu. Information Sciences, 2008
1,012 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/0ebb7307294561c6aaed3bcd51a19edd35111999
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
- Witold Pedrycz
- Pradipta Maji
- Athanasios V. Vasilakos
- Partha Garai
- Alper Ekrem Murat
- Alper Ünler
- Jingtao Yao
- Yiyu Yao
In Computer Vision and Pattern Recognition
- Witold Pedrycz
- Pradipta Maji
- Athanasios V. Vasilakos
- Partha Garai
- Ratna Babu Chinnam
- Alper Ekrem Murat
- Alper Ünler
- Jingtao Yao
In Information Systems
Citations in the corpus
- mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification (2011, methodology): “In addition, there are feature selection methods based on rough set theory [4,7,19,58,63] and on Boolean independent component analysis [1].”
- Fuzzy–Rough Simultaneous Attribute Selection and Feature Extraction Algorithm (2013, methodology): “Given an arbitrary object xi ∈ U and P ⊆ C, the neighborhood ΦP(xi) of xi with given threshold Φ, in feature space P, is defined as [19]”
- Granular Computing: Perspectives and Challenges (2013, methodology): “Distance measures are used in cluster analysis to determine whether or not objects belong to a cluster [17], [31].”