Intuitionistic L-fuzzy β-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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Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Complexity ◽

10.1155/2017/8986917 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 6

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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Fuzzy covering rough set model on dual-universes

International Conference on Automatic Control and Artificial Intelligence (ACAI 2012) ◽

10.1049/cp.2012.1167 ◽

2012 ◽

Author(s):

Mengting Shi ◽

Wenqi Liu ◽

Gaofeng Yu ◽

Lei Li

Keyword(s):

Rough Set ◽

Covering Rough Set

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Degree Induced Covering Rough Set Model

Advanced Technology in Teaching - Proceedings of the 2009 3rd International Conference on Teaching and Computational Science (WTCS 2009) - Advances in Intelligent and Soft Computing ◽

10.1007/978-3-642-11276-8_110 ◽

2012 ◽

pp. 829-833

Author(s):

Chen Dingjun ◽

Li Li ◽

Wu Kaiteng

Keyword(s):

Rough Set ◽

Covering Rough Set

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Two Types of Intuitionistic Fuzzy Covering Rough Sets and an Application to Multiple Criteria Group Decision Making

Symmetry ◽

10.3390/sym10100462 ◽

2018 ◽

Vol 10 (10) ◽

pp. 462 ◽

Cited By ~ 11

Author(s):

Jingqian Wang ◽

Xiaohong Zhang

Keyword(s):

Decision Making ◽

Rough Set ◽

Group Decision Making ◽

Rough Sets ◽

Group Decision ◽

Multiple Criteria ◽

Approximation Spaces ◽

Intuitionistic Fuzzy ◽

Definition Of ◽

Covering Rough Set

Intuitionistic fuzzy rough sets are constructed by combining intuitionistic fuzzy sets with rough sets. Recently, Huang et al. proposed the definition of an intuitionistic fuzzy (IF) β -covering and an IF covering rough set model. In this paper, some properties of IF β -covering approximation spaces and the IF covering rough set model are investigated further. Moreover, we present a novel methodology to the problem of multiple criteria group decision making. Firstly, some new notions and properties of IF β -covering approximation spaces are proposed. Secondly, we study the characterizations of Huang et al.’s IF covering rough set model and present a new IF covering rough set model for crisp sets in an IF environment. The relationships between these two IF covering rough set models and some other rough set models are investigated. Finally, based on the IF covering rough set model, Huang et al. also defined an optimistic multi-granulation IF rough set model. We present a novel method to multiple criteria group decision making problems under the optimistic multi-granulation IF rough set model.

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