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Improved rough approximations based on variable J-containment neighborhoods.
1School of Mathematical Sciences, Anhui University, 111 Jiulong Road, Hefei, 230601 Anhui People's Republic of China.
Granular Computing
|April 16, 2024
Summary
This study introduces variable j-containment neighborhoods, a flexible approach to rough set theory that ensures reflexivity and adjustable granularity for improved attribute reduction in incomplete information systems.
Area of Science:
- Data Science
- Artificial Intelligence
- Set Theory
Background:
- Classic generalized rough set models in neighborhood systems offer a broad framework but can lack reflexivity.
- Existing neighborhood types (adhesion, containment, j-neighborhoods) have limitations in reflexivity and granularity (too fine or too coarse).
Purpose of the Study:
- To propose a novel neighborhood construction: variable j-containment neighborhoods (VjCNs).
- To ensure reflexivity and flexible granularity in neighborhood spaces for rough set applications.
- To enhance attribute reduction in incomplete information systems.
Main Methods:
- Designed variable j-containment neighborhoods (VjCNs) satisfying reflexivity and adjustable granularity.
- Generalized three types of rough approximations within VjCN spaces.
- Analyzed topological structures based on VjCNs and compared with existing methods.
Main Results:
- VjCNs provide flexible granularity adjustment through a parameter 'j'.
- The proposed rough set model demonstrates advantages in attribute reduction for incomplete data.
- The approach ensures reflexivity, addressing limitations of previous neighborhood types.
Conclusions:
- Variable j-containment neighborhoods offer a flexible and reflexive framework for rough set theory.
- The VjCN model enhances attribute reduction, particularly in incomplete information systems.
- This novel approach provides a more adaptable granularity compared to existing neighborhood systems.
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