Q
Qinghua Hu
Researcher at Tianjin University
Publications - 534
Citations - 21690
Qinghua Hu is an academic researcher from Tianjin University. The author has contributed to research in topics: Rough set & Feature selection. The author has an hindex of 62, co-authored 472 publications receiving 14060 citations. Previous affiliations of Qinghua Hu include Hebei Normal University & Huazhong University of Science and Technology.
Papers
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Journal ArticleDOI
A Fitting Model for Feature Selection With Fuzzy Rough Sets
TL;DR: A parameterized fuzzy relation is introduced to characterize the fuzzy information granules, using which the fuzzy lower and upper approximations of a decision are reconstructed and a new fuzzy rough set model is introduced.
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Multi-label feature selection based on max-dependency and min-redundancy
TL;DR: This paper considers the two factors of multi-label feature, feature dependency and feature redundancy, and proposes an evaluation measure that combines mutual information with a max-dependency and min-redundancy algorithm, which allows to select superior feature subset for multi- label learning.
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Subspace clustering guided unsupervised feature selection
TL;DR: Experimental results on benchmark datasets for unsupervised feature selection show that SCUFS outperforms the state-of-the-art UFS methods and can uncover the underlying multi-subspace structure of data.
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Feature selection with test cost constraint
Fan Min,Qinghua Hu,William Zhu +2 more
TL;DR: The feature selection with test cost constraint problem for this issue, which has a simple form while described as a constraint satisfaction problem (CSP), and some existing feature selection problems in rough sets, especially in decision-theoretic rough sets are defined from the viewpoint of CSP.
Journal ArticleDOI
A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets
TL;DR: The proposed algorithms to find reducts that are based on the minimal elements in the discernibility matrix are developed in the framework of fuzzy rough sets and Experimental comparison shows that the proposed algorithms are effective.