Unsupervised attribute reduction based on variable precision weighted neighborhood dependency

Unsupervised attribute reduction based on variable precision weighted neighborhood dependency

Summary: Neighborhood rough set (NRS) have been successfully applied to attribute reduction (AR). However, most current methods of AR based on NRS are supervised or semi-supervised. This limits their ability to process data without decision information. When granulating data samples, NRS considers o...

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Bibliographic Details
Main Authors: Yi Li, Benwen Zhang, Hongming Mo, Jiancheng Hu, Yuncheng Liu, Xingqiang Tan
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:iScience
Subjects:
Computer science
Computational mathematics
Artificial intelligence
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004224024957

Internet

http://www.sciencedirect.com/science/article/pii/S2589004224024957

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