M
Mingwen Shao
Researcher at The Chinese University of Hong Kong
Publications - 13
Citations - 1063
Mingwen Shao is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Rough set & Fuzzy logic. The author has an hindex of 10, co-authored 12 publications receiving 631 citations. Previous affiliations of Mingwen Shao include China University of Petroleum.
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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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Feature subset selection based on fuzzy neighborhood rough sets
TL;DR: This paper constructs a novel rough set model for feature subset selection, and defines the dependency between fuzzy decision and condition attributes and employ the dependency to evaluate the significance of a candidate feature, using which a greedyfeature subset selection algorithm is designed.
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Feature Selection Based on Neighborhood Self-Information
TL;DR: It is proven that the fourth measure, called relative neighborhood self-information, is better for feature selection than the other measures, because not only does it consider both the lower and the upper approximations but also the change of its magnitude is largest with the variation of feature subsets.
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Fuzzy rough set-based attribute reduction using distance measures
TL;DR: This study introduces distance measures into fuzzy rough sets and proposes a novel method for attribute reduction by constructing a fuzzy rough set model based on distance measure with a fixed parameter and replacing it with a variable parameter.
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Fuzzy Rough Attribute Reduction for Categorical Data
TL;DR: A new fuzzy-rough-set model for categorical data is proposed by introducing a variable parameter to control the similarity of samples by employing the iterative computation strategy to define fuzzy rough approximations and dependence functions.