scholarly journals Adjustable Fuzzy Rough Reduction: A Nested Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ying Shi ◽  
Hui Qi ◽  
Xiaofang Mu ◽  
Mingxing Hou
Keyword(s):  
Rough Set ◽  
Heuristic Algorithm ◽  
Computational Efficiency ◽  
Attribute Reduction ◽  
Fuzzy Rough Set ◽  
Attribute Evaluation ◽  
Traditional Approaches

As a crucial extension of Pawlak's rough set, a fuzzy rough set has been successfully applied in real-valued attribute reduction. Nevertheless, the traditional fuzzy rough set is not provided with adjustable ability due to the maximal and minimal operators. It follows that the associated measure for attribute evaluation is not always appropriate. To alleviate such problems, a novel adjustable fuzzy rough set model is presented and further introduced into the parameterized attribute reduction. Additionally, the inner relationship between the appointed parameter and the reduct result is discovered, and thereby a nested mechanism is adopted to accelerate the searching procedure of reduct. Experiments demonstrate that the proposed heuristic algorithm can offer us more stable reducts with higher computational efficiency as compared with the traditional approaches.

Fuzzy rough set based incremental attribute reduction from dynamic data with sample arriving

2017 ◽  
Vol 312 ◽  
pp. 66-86 ◽  
Author(s):  
Yanyan Yang ◽  
Degang Chen ◽  
Hui Wang ◽  
Eric C.C. Tsang ◽  
Deli Zhang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Fuzzy Rough Set ◽  
Dynamic Data

scholarly journals About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem

2016 ◽  
Vol 16 (4) ◽  
pp. 13-28 ◽  
Author(s):  
Cao Chinh Nghia ◽  
Demetrovics Janos ◽  
Nguyen Long Giang ◽  
Vu Duc Thi
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Data Sets ◽  
Theory Approach ◽  
Fuzzy Rough Set ◽  
Fuzzy Distance ◽  
Reduction Methods ◽  
Fuzzy Partitions ◽  
Decision Tables

Abstract According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.

scholarly journals Neighborhood Attribute Reduction: A Multicriterion Strategy Based on Sample Selection

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 282 ◽  
Author(s):  
Yuan Gao ◽  
Xiangjian Chen ◽  
Xibei Yang ◽  
Pingxin Wang
Keyword(s):  
Rough Set ◽  
Heuristic Algorithm ◽  
Sample Selection ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Multiple Criteria ◽  
Experimental Results ◽  
Time Consumption ◽  
Redundant Data ◽  
Selection For

In the rough-set field, the objective of attribute reduction is to regulate the variations of measures by reducing redundant data attributes. However, most of the previous concepts of attribute reductions were designed by one and only one measure, which indicates that the obtained reduct may fail to meet the constraints given by other measures. In addition, the widely used heuristic algorithm for computing a reduct requires to scan all samples in data, and then time consumption may be too high to be accepted if the size of the data is too large. To alleviate these problems, a framework of attribute reduction based on multiple criteria with sample selection is proposed in this paper. Firstly, cluster centroids are derived from data, and then samples that are far away from the cluster centroids can be selected. This step completes the process of sample selection for reducing data size. Secondly, multiple criteria-based attribute reduction was designed, and the heuristic algorithm was used over the selected samples for computing reduct in terms of multiple criteria. Finally, the experimental results over 12 UCI datasets show that the reducts obtained by our framework not only satisfy the constraints given by multiple criteria, but also provide better classification performance and less time consumption.

Fuzzy rough set based attribute reduction for information systems with fuzzy decisions

2011 ◽  
Vol 24 (5) ◽  
pp. 689-696 ◽  
Author(s):  
Qiang He ◽  
Congxin Wu ◽  
Degang Chen ◽  
Suyun Zhao
Keyword(s):  
Information Systems ◽  
Rough Set ◽  
Attribute Reduction ◽  
Fuzzy Rough Set
Sign in / Sign up

Export Citation Format

Share Document