Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data
Attribute reduction is an important research content of the rough set theory.Its main purpose is to eliminate irrelevant attributes in information systems, reduce data dimensions and improve data knowledge discovery performance.However, most of the attribute reduction methods based on a rough set do...
Saved in:
| Main Author: | Haibo LAN |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2022-07-01
|
| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2022066 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Incremental attribute reduction algorithm for dominance-based neighborhood relative decision entropy
by: CHEN Baoguo, et al.
Published: (2024-01-01) -
On Dynamical Measures of Quantum Information
by: James Fullwood, et al.
Published: (2025-03-01) -
infomeasure: a comprehensive Python package for information theory measures and estimators
by: Carlson Moses Büth, et al.
Published: (2025-08-01) -
A Heuristic Attribute-Reduction Algorithm Based on Conditional Entropy for Incomplete Information Systems
by: Yanling Bao, et al.
Published: (2024-10-01) -
Information decomposition of airport dynamics: A study of Europe and US
by: Kishor Acharya, et al.
Published: (2025-05-01)