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A dynamic attribute reduction algorithm based on relative neighborhood discernibility degree. [PDF]
Sci RepFeng W, Sun T.
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Subset neighborhood rough sets
Knowledge-Based Systems, 2022We present a novel kind of neighborhood, named subset neighborhood and denoted as Sρ-neighborhood. It is defined under an arbitrary binary relation using the inclusion relations between Nρ-neighborhoods. We study its relationships with some kinds of neighborhood systems given in the literature.
Tareq M. Al-shami, Davide Ciucci
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NMGRS: Neighborhood-based multigranulation rough sets
International Journal of Approximate Reasoning, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuhua Qian, Jinjin Li
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Knowledge-Based Systems, 2018
Abstract With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features.
Jiye Liang, Yuhua Qian, Qian Guo
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Abstract With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features.
Jiye Liang, Yuhua Qian, Qian Guo
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Neighborhood Rough Set Approach With Biometric Application
International Journal of Sociotechnology and Knowledge Development, 2022This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds
B. Lavanya +2 more
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Covering rough sets based on neighborhoods: An approach without using neighborhoods
International Journal of Approximate Reasoning, 2011 Rough set theory, a mathematical tool to deal with inexact or uncertain knowledge in information systems, has originally described the indiscernibility of elements by equivalence relations. Covering rough sets are a natural extension of classical rough sets by relaxing the partitions arising from equivalence relations to coverings.
Ping Zhu
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Measures of uncertainty for neighborhood rough sets
Knowledge-Based Systems, 2017Uncertainty measures are critical evaluating tools in machine learning fields, which can measure the dependence and similarity between two feature subsets and can be used to judge the significance of features in classifying and clustering algorithms. In the classical rough sets, there are some uncertainty tools to measure a feature subset, including ...
Yu Xue, Yumin Chen
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A soft neighborhood rough set model and its applications
Information Sciences, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuang An +4 more
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Neighborhood Systems: Rough Set Approximations and Definability
Fundamenta Informaticae, 2018The notions of approximation and definability in classical rough set theory and their generalizations have received much attention. In this paper, we study such generalizations from the perspective of neighborhood systems. We introduce four different types of definability, called interior definability, closure definability, interior-closure (IC ...
Yu-Ru Syau, En-Bing Lin, Churn-Jung Liau
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A Fast Attribute Reduction Algorithm of Neighborhood Rough Set
2021 13th International Conference on Knowledge and Smart Technology (KST), 2021The classic Pawlak rough set model is only suitable for processing discrete data, not continuous data directly. Pawlak rough set model needs discretization when processing continuous data, which will cause the loss of information in the data set. In this context, Hu proposed the neighborhood rough set (NRS) model, which greatly expanded the application
Wenhua Li, Shuyin Xia, Zizhong Chen
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