Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model
Attribute reduction is the core research content in rough set theory. At present, the attribute reduction of numerical information system mostly adopts the neighborhood rough set method. In order to further improve the similarity measurement effect between data objects, kernel function method is use...
Saved in:
Main Authors: | Baoguo Chen, Lei Chen, Ming Deng |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Journal of Mathematics |
Online Access: | http://dx.doi.org/10.1155/2022/5675200 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Uncertainty Analysis of Knowledge Reductions in Rough Sets
by: Ying Wang, et al.
Published: (2014-01-01) -
δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions
by: Hengrong Ju, et al.
Published: (2014-01-01) -
Attribute Reduction Based on Consistent Covering Rough Set and Its Application
by: Jianchuan Bai, et al.
Published: (2017-01-01) -
Rough sets theory and its extensions for attribute reduction: a review
by: Sadegh Eskandari
Published: (2021-06-01) -
Multigranulations Rough Set Method of Attribute Reduction in Information Systems Based on Evidence Theory
by: Minlun Yan
Published: (2014-01-01)