Minimal Attribute Reduction for Rough Set Based on Attribute Enumeration Tree

2012 ◽  
Vol 4 (19) ◽  
pp. 391-399
Author(s):  
Jiang Yu
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Enumeration Tree

scholarly journals Parametric Matroid of Rough Set

2015 ◽  
Vol 23 (06) ◽  
pp. 893-908 ◽  
Author(s):  
Yanfang Liu ◽  
Hong Zhao ◽  
William Zhu
Keyword(s):  
Rough Set ◽  
Equivalence Relation ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Independent Set ◽  
Approximation Operator ◽  
Approximation Number ◽  
Lower Approximation ◽  
The One

Rough set is mainly concerned with the approximations of objects through an equivalence relation on a universe. Matroid is a generalization of linear algebra and graph theory. Recently, a matroidal structure of rough sets is established and applied to the problem of attribute reduction which is an important application of rough set theory. In this paper, we propose a new matroidal structure of rough sets and call it a parametric matroid. On the one hand, for an equivalence relation on a universe, a parametric set family, with any subset of the universe as its parameter, is defined through the lower approximation operator. This parametric set family is proved to satisfy the independent set axiom of matroids, therefore a matroid is generated, and we call it a parametric matroid of the rough set. Through the lower approximation operator, three equivalent representations of the parametric set family are obtained. Moreover, the parametric matroid of the rough set is proved to be the direct sum of a partition-circuit matroid and a free matroid. On the other hand, partition-circuit matroids are well studied through the lower approximation number, and then we use it to investigate the parametric matroid of the rough set. Several characteristics of the parametric matroid of the rough set, such as independent sets, bases, circuits, the rank function and the closure operator, are expressed by the lower approximation number.

Research on RS-WNN Integrated Modeling

2011 ◽  
Vol 105-107 ◽  
pp. 2169-2173
Author(s):  
Zong Chang Xu ◽  
Xue Qin Tang ◽  
Shu Feng Huang
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Attribute Reduction ◽  
Wavelet Neural Network ◽  
Integrated Modeling ◽  
Generalization Ability ◽  
Decision Attributes ◽  
Optimum Decision ◽  
Structure Complexity ◽  
Modeling Algorithm

Wavelet Neural Network (WNN) integration modeling based on Rough Set (RS) is studied. An integration modeling algorithm named RS-WNN, which first introduces a heuristic attribute reduction recursion algorithm to determine the optimum decision attributes and then conducts WNN modeling, is proposed. This method is adopted to more effectively eliminate the redundant attributes, lower the structure complexity of WNN, which reduce the time of training and improve the generalization ability of WNN. The result of the experiment shows this method is superior and efficient.

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.

scholarly journals Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianchuan Bai ◽  
Kewen Xia ◽  
Yongliang Lin ◽  
Panpan Wu
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Processing Step ◽  
Important Processing ◽  
Continuous Attribute ◽  
Covering Rough Set

As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information. Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both theory and application. By using the covering rough set, the process of continuous attribute discretization can be avoided. Firstly, this paper focuses on consistent covering rough set and reviews some basic concepts in consistent covering rough set theory. Then, we establish the model of attribute reduction and elaborate the steps of attribute reduction based on consistent covering rough set. Finally, we apply the studied method to actual lagging data. It can be proved that our method is feasible and the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) and Relevance Vector Machine (RVM). Furthermore, the recognition results are consistent with the actual test results of a gas well, which verifies the effectiveness and efficiency of the presented method.

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