Results 31 to 40 of about 3,229 (278)
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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The Journal of Engineering, 2019 It is an important subject to mine valuable knowledge from complex and massive data in the era of big data. Rough set theory is a new mathematical tool for dealing with uncertain and inaccurate data, decision-theoretic rough set model (DTRS), as an ...
Jiajun Chen +3 more
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Multi-Label Attribute Reduction Based on Neighborhood Multi-Target Rough Sets
, 2022 The rough set model has two symmetry approximations called upper approximation and lower approximation, which correspond to a concept’s intension and extension, respectively.
Jinjin Li +3 more
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Six-Set Approximation Theorem of neighborhood related rough sets
International Journal of Approximate Reasoning, 2019 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liwen Ma, Hui Lu
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Neighborhood Rough-Sets-Based Spatial Data Analytics
, 2018 Rough Set Theory partitions a universe using single layered granulation. The equivalence classes induced by rough sets are based on discretised values. Considering the fact that the spatial data are continuous at large, discretising them may cause loss ...
Sharmila Banu K., B. K. Tripathy
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Rough sets meet statistics : a new view on rough set reasoning about numerical data
, 2020 In this paper, we present a new view on how the concept of rough sets may be interpreted in terms of statistics and used for reasoning about numerical data.
Chris Cornelis +7 more
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Applied Sciences, 2023 Existing 3D city reconstruction via oblique photography can only produce surface models, lacking semantic information about the urban environment and the ability to incorporate all individual buildings.
Wenqiang Xu, Yongnian Zeng, Changlin Yin
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Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets
IEEE Access, 2020 Multi-label learning is often applied to handle complex decision tasks, and feature selection is its essential part. The relation of labels is always ignored or not enough to consider for both multi-label learning and its feature selection.
Zhixuan Deng +5 more
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FEBS Letters, EarlyView.We present robust protocols for the preparation of supported lipid bilayers (SLBs) incorporating either Salmonella smooth LPS or outer membrane vesicles (OMVs). We use a combination of quartz crystal microbalance with dissipation (QCM‐D) and fluorescence microscopy to both characterize the SLBs of various compositions and to probe their interactions ...
Hudson P. Pace +6 more
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, 2022 Part 1: Machine LearningInternational audienceFeature selection is an effective method for dimensionality reduction in machine learning and data mining.
Xu, Jiucheng +3 more
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