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Neighborhood operators for covering-based rough sets
Information Sciences, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
D'eer, Lynn +3 more
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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 ...
Yumin Chen, Yu Xue, Ying Ma, Feifei Xu
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Attribute reduction based on k-nearest neighborhood rough sets
International Journal of Approximate Reasoning, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Changzhong Wang +3 more
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Variable radius neighborhood rough sets and attribute reduction
International Journal of Approximate Reasoning, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Di, Zhu, Ping
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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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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.
Qi Wang +4 more
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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.
Qi Wang +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 ...
Syau, Yu-Ru +2 more
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Neighborhood Systems and Variable Precision Generalized Rough Sets
Fundamenta Informaticae, 2017In this paper, we present the connection between the concepts of Variable Precision Generalized Rough Set model (VPGRS-model) and Neighborhood Systems through binary relations. We provide characterizations of lower and upper approximations for VPGRS-model by introducing minimal neighborhood systems.
Syau, Yu-Ru +2 more
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Knowledge Granulation Based Roughness Measure for Neighborhood Rough Sets
2013 Third International Conference on Intelligent System Design and Engineering Applications, 2013Neighborhood rough sets have been applied to feature selection and attribute reduction successfully. Roughness is an important uncertainty measure for a concept in an information system. In this paper, generalized from the classical roughness, a new uncertainty measure based on granulation of knowledge for neighborhood rough sets is proposed to ...
Chengdong Yang +2 more
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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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