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.

Discernibility Matrix-Based Attribute Reduction Algorithm of Decision Table

2012 ◽  
Vol 457-458 ◽  
pp. 1230-1234 ◽  
Author(s):  
Ying He ◽  
Dan He
Keyword(s):  
Attribute Reduction ◽  
Attribute Selection ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Discernibility Matrix

A discernibility matrix-based attribute reduction algorithm of decision table is introduced in this paper, which takes the importance of attributes as the heuristic message. This method solves the problem of the attribute selection when the frequencies of decision table attributes are equal. The result shows that this method can give out simple but effective method of attribute reduction.

A Heuristic Reduction Algorithm Based on the Feature Weight

2014 ◽  
Vol 1070-1072 ◽  
pp. 2051-2055
Author(s):  
Xiao Xue Xing ◽  
Li Min Du ◽  
Wei Wei Shang
Keyword(s):  
Attribute Reduction ◽  
Reduction Algorithm ◽  
Discernibility Matrix ◽  
Heuristic Knowledge ◽  
Feature Weight ◽  
The Mean ◽  
Mean Time

The basic attribute reduction algorithm based on discernibility matrix was introduced. Through analyzing the algorithm, the shortages were found. Then the heuristic reduction algorithm based on the feature weight is presented in the paper. In the algorithm, the discernibility matrix and the heuristic knowledge are combined toghther. It can be proved that the proposed algorithm is more intuitive and easier in computation. At the mean time the speed of the reduction algorithm could be improved.

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