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Multilabel Feature Selection Based on Fisher Score with Center Shift and Neighborhood IntuitionisticFuzzy Entropy [PDF]
Jisuanji kexueThe edge samples in the existing multilabel Fisher score models affect the classification effect of the algorithm.It has the available virtues of stronger expression and resolution when using neighborhood intuitive fuzzy entropy to deal with uncertain ...
SUN Lin, MA Tianjiao
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Neighborhood Granule Classifiers
Applied Sciences, 2018 Classifiers are divided into linear and nonlinear classifiers. The linear classifiers are built on a basis of some hyper planes. The nonlinear classifiers are mainly neural networks. In this paper, we propose a novel neighborhood granule classifier based
Hongbo Jiang, Yumin Chen
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Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [PDF]
Jisuanji kexue, 2022 In multi-label learning and classification, existing feature selection algorithms based on neighborhood rough sets will use classification margin of samples as the neighborhood radius.However, when the margin is too large, the classification may be ...
SUN Lin, HUANG Miao-miao, XU Jiu-cheng
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Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets
International Journal of Computational Intelligence Systems, 2021 Rough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance.
Yingyue Chen, Yumin Chen
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Single-Valued Neutrosophic Covering-Based Rough Set Model Over Two Universes and Its Application in MCDM [PDF]
Neutrosophic Sets and Systems, 2023 This article aims to propose a new type of single-valued neutrosophic(SVN) covering-based rough sets over two universes by using Wang’s single-valued neutrosophic covering rough sets.
Somen Debnath
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Attribute Reduction of Boolean Matrix in Neighborhood Rough Set Model
International Journal of Computational Intelligence Systems, 2020 Neighborhood rough set is a powerful tool to deal with continuous value information systems. Graphics processing unit (GPU) computing can efficiently accelerate the calculation of the attribute reduction and approximation sets based on matrix.
Yan Gao, Changwei Lv, Zhengjiang Wu
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Sensor data reduction with novel local neighborhood information granularity and rough set approach
Frontiers in Physics, 2023 Data description and data reduction are important issues in sensors data acquisition and rough sets based models can be applied in sensors data acquisition. Data description by rough set theory relies on information granularity, approximation methods and
Xiaoxue Fan +6 more
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Granule Vectors and Granular Convolutional Classifiers
IEEE Access, 2020 Convolutional operations can extract effective features and have been widely used in the field of deep learning. For the deficiency of convolution mainly dealing with numerical data, we propose a novel convolutional operator on granules with a set form ...
Yumin Chen +3 more
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Quantitative dominance-based neighborhood rough sets via fuzzy preference relations
, 2021 Dominance relations exist extensively in decision-making problems. Dominance-based neighborhood rough sets (DNRS) using fuzzy preference relations (FPRs) are presented in this article to deal with attribute reduction in the large-scale decision-making ...
Shi, Guang +5 more
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Novel Graph Neighborhoods Emerging from Ideals
Mathematics, 2023 Rough set theory is a mathematical approach that deals with the problems of uncertainty and ambiguity in knowledge. Neighborhood systems are the most effective instruments for researching rough set theory in general.
Ayşegül Çaksu Güler +4 more
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