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Kernel Neighborhood Rough Sets Model and Its Application [PDF]
Complexity, 2018 Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation.
Kai Zeng, Siyuan Jing
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Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm [PDF]
Fracture and Structural Integrity, 2023 S-N curve fatigue samples of titanium alloy welded joints have such a comparatively significant scatter, that results in the issue that the fatigue life prediction accuracy is not optimal.
Yangjinyu Li, Li Zou, Zhengjie Zhu
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A Neighborhood Rough Classification with Dempster-Shafer Evidence Theory
Nantong Daxue xuebao. Ziran kexue ban, 2021 In order to further improve the classification mechanism and the performance of neighborhood classifier, a Dempster-Shafer(D-S) evidence-driven neighborhood rough classification method is proposed.
JU Hengrong; DING Weiping; YIN Tao; FAN Zhe; LIU Jiubing
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On Topological Properties of Generalized Rough Approximation Operators [PDF]
Jisuanji kexue, 2022 Rough set theory is a mathematical tool for dealing with uncertain information.The core notions of rough set theory is approximation operators of approximation spaces.Pawlak approximation operators are established by using equivalence relations on the ...
LI Yan-yan, QIN Ke-yun
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IEEE Access, 2020 Neighborhood rough model is widely used in feature selection with high dimension, fuzzy, continuous and discrete attributes, incomplete data and so on, and the application of neighborhood rough model depends on neighborhood threshold.
Yanan Hu +4 more
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An Ensemble Classification Method for High-Dimensional Data Using Neighborhood Rough Set
Complexity, 2021 Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data.
Jing Zhang +3 more
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Complex & Intelligent Systems, 2023 Fuzzy rough set models are useful tools for dealing with fuzzy and real-valued data. They have been used in many real-world applications. In this paper, we investigate the fuzzy rough set model based on triangular norms and fuzzy implications.
Zhaohao Wang
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Attribute Reduction with Fuzzy Weighted Neighborhood Rough Sets [PDF]
International Journal of Mathematical, Engineering and Management SciencesNeighborhood rough sets (NRSs) have been widely recognized as an efficient tool for solving attribute reduction problems in numerical data. In constructing neighborhood information granules, many existing extensions introduce weighting mechanisms for ...
Pham Viet Anh +4 more
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Multi-Label Attribute Reduction Based on Variable Precision Fuzzy Neighborhood Rough Set
IEEE Access, 2020 Multi-label attribute reduction as a common dimensionality reduction technique has obtained widely research in recent years. Most existing multi-label attribute reduction methods adopt discretization to deal with mixed data and have strict requirements ...
Panpan Chen, Menglei Lin, Jinghua Liu
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A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment
Complex & Intelligent Systems, 2023 Healthcare tends to be one of the most complicated sectors, and hospitals exist at the core of healthcare activities. One of the most significant elements in hospitals is service quality level.
He Li +6 more
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