scholarly journals WNN Nonlinear Modeling Method based on Heuristic RS Attribute Reduction Algorithm and Its Application

2017 ◽  
Keyword(s):  
Nonlinear Modeling ◽  
Attribute Reduction ◽  
Modeling Method ◽  
Reduction Algorithm

Application of an attribute reduction algorithm based on discernibility matrix in PBNM

2014 ◽  
Author(s):  
Shuo Feng ◽  
Haiying Chu ◽  
Xuyang Wang ◽  
Yuanka Liang ◽  
Xianwei Shi ◽  
...  
Keyword(s):  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Discernibility Matrix

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.

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