Results 281 to 290 of about 1,718,703 (323)

Feature selection for imbalanced data based on neighborhood rough sets

Information Sciences, 2019
Feature selection is a meaningful aspect of data mining that aims to select more relevant data features and provide more concise and explicit data descriptions. It is beneficial for constructing an effective learning model and reducing the consumption of
Hongmei Chen, Tianrui Li, Xin-Nan Fan, Chuan Luo   +3 more
semanticscholar   +1 more source

Exploring Neighborhood Structures with Neighborhood Rough Sets in Classification Learning

2013
We introduce neighborhoods of samples to granulate the universe and use the neighborhood granules to approximate classification, thus they derived a model of neighborhood rough sets. Some machine learning algorithms, including boundary sample selection, feature selection and rule extraction, were developed based on the model.
Qinghua Hu, Leijun Li, Pengfei Zhu
openaire   +1 more source

Pseudo-label neighborhood rough set: Measures and attribute reductions

International Journal of Approximate Reasoning, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xibei Yang, Shaochen Liang, Hualong Yu, Shang Gao 0001, Yuhua Qian   +4 more
openaire   +3 more sources

Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification

Information Sciences, 2021
Lin Sun, Tianxiang Wang, Weiping Ding, Jiucheng Xu, Yaojin Lin   +4 more
semanticscholar   +1 more source

GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set

IEEE Transactions on Neural Networks and Learning Systems
Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent ...
Shuyin Xia, Cheng Wang 0050, Guoyin Wang 0001, Xinbo Gao 0001, Weiping Ding 0001, JianHang Yu, Yujia Zhai, Zizhong Chen   +7 more
openaire   +2 more sources

A Framework for Feature Construction Based on Neighborhood Rough Set

2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2021
Yang Chen, Xin Yang 0012, Jiani Zheng, Wei Wang 0336   +3 more
openaire   +1 more source

Maximum relevance minimum redundancy-based feature selection using rough mutual information in adaptive neighborhood rough sets

Applied intelligence (Boston), 2023
Kanglin Qu, Jiucheng Xu, Z. Han, Shihui Xu   +3 more
semanticscholar   +1 more source

Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

IEEE Transactions on Fuzzy Systems, 2021
Yuhua Qian, Weiping Ding, Lanying Wang   +2 more
exaly  

An incremental approach to feature selection using the weighted dominance-based neighborhood rough sets

International Journal of Machine Learning and Cybernetics, 2022
Yanzhou Pan, Weihua Xu, Qinwen Ran
semanticscholar   +1 more source

A neighborhood rough sets-based ensemble method, with application to software fault prediction

Expert systems with applications
Feng Jiang, Qiang Hu, Zhiyong Yang, Jinhuan Liu, Junwei Du   +4 more
semanticscholar   +1 more source