research-article
Authors: Jinjin Zhang, Ke Sun, Bing Huang, Tianxing Wang, Xin Wang
Volume 265, Issue C
Published: 18 February 2025 Publication History
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Abstract
Attribute reduction is a key step in processing large-scale datasets, where the Granular Ball Neighborhood Rough Set (GBNRS) can significantly enhance the performance of attribute reduction compared to the traditional Neighborhood Rough Set (NRS). However, the GBNRS algorithm faces such challenges as a sharp increase in computational costs in high-dimensional spaces. To address these issues, this study introduces a new granular ball quality index to judge the separability degree of decision classes, and on the basis of this index, a rapid variable granular ball generation model (RVGBGM) is proposed. Compared with GBNRS, RVGBGM has the following advantages: (1) it reduces the number of granular balls and can quickly reflect the separability degree of different decision classes with few granular balls, (2) it constructs granular balls by using label information and shortens the time of granular ball construction, and (3) it can adjust the radius of granular balls adaptively by using parameters to determine the optimal granular ball radius for different datasets. Finally, we compare the RVGBGM algorithm with classical attribute reduction algorithms and the current state-of-the-art granular ball algorithm on 11 datasets. The proposed algorithm significantly improves algorithm efficiency while maintaining high accuracy.
Highlights
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A variable granular ball is defined.
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A novel performance measure called separability degree between two variable granular balls is presented.
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An efficient attribute reduction algorithm based on variable granular ball is devised and verified.
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Index Terms
Attribute reduction based on a rapid variable granular ball generation model
Computing methodologies
Machine learning
Learning paradigms
Machine learning approaches
Information systems
Data management systems
Database design and models
Information systems applications
Data mining
Theory of computation
Theory and algorithms for application domains
Index terms have been assigned to the content through auto-classification.
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Published In
Expert Systems with Applications: An International Journal Volume 265, Issue C
Mar 2025
1566 pages
Issue’s Table of Contents
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 18 February 2025
Author Tags
- Attribute reduction
- Granular ball generation
- Rapid variable granular ball generation model
- Separability degree
Qualifiers
- Research-article
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