An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems

2019 ◽  
Vol 5 (4) ◽  
pp. 545-559 ◽  
Author(s):  
Chucai Zhang ◽  
Jianhua Dai
Keyword(s):  
Attribute Reduction ◽  
Decision Systems ◽  
Knowledge Granularity ◽  
Reduction Approach

An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view

2017 ◽  
Vol 411 ◽  
pp. 23-38 ◽  
Author(s):  
Yunge Jing ◽  
Tianrui Li ◽  
Hamido Fujita ◽  
Zeng Yu ◽  
Bin Wang
Keyword(s):  
Attribute Reduction ◽  
Knowledge Granularity ◽  
Reduction Approach

Attribute reduction in fuzzy multi-covering decision systems via observational- consistency and fuzzy discernibility

2021 ◽  
pp. 1-15
Author(s):  
Rongde Lin ◽  
Jinjin Li ◽  
Dongxiao Chen ◽  
Jianxin Huang ◽  
Yingsheng Chen
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Recursive Method ◽  
Redundant Information ◽  
Reduction Algorithm ◽  
Discernibility Matrix ◽  
Decision Systems ◽  
Theoretical Tool ◽  
Lower Ratio ◽  
Covering Rough Set

Fuzzy covering rough set model is a popular and important theoretical tool for computation of uncertainty, and provides an effective approach for attribute reduction. However, attribute reductions derived directly from fuzzy lower or upper approximations actually still occupy large of redundant information, which leads to a lower ratio of attribute-reduced. This paper introduces a kind of parametric observation sets on the approximations, and further proposes so called parametric observational-consistency, which is applied to attribute reduction in fuzzy multi-covering decision systems. Then the related discernibility matrix is developed to provide a way of attribute reduction. In addition, for multiple observational parameters, this article also introduces a recursive method to gradually construct the multiple discernibility matrix by composing the refined discernibility matrix and incremental discernibility matrix based on previous ones. In such case, an attribute reduction algorithm is proposed. Finally, experiments are used to demonstrate the feasibility and effectiveness of our proposed method.

Neighborhood attribute reduction approach to partially labeled data

2018 ◽  
Vol 5 (2) ◽  
pp. 239-250 ◽  
Author(s):  
Keyu Liu ◽  
Eric C. C. Tsang ◽  
Jingjing Song ◽  
Hualong Yu ◽  
Xiangjian Chen ◽  
...  
Keyword(s):  
Attribute Reduction ◽  
Partially Labeled Data ◽  
Reduction Approach
Sign in / Sign up

Export Citation Format

Share Document