scholarly journals Ranking for Objects and Attribute Reductions in Intuitionistic Fuzzy Ordered Information Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
Xiaoyan Zhang ◽  
Weihua Xu
Keyword(s):  
Information Systems ◽  
Knowledge Representation ◽  
Rough Set ◽  
Attribute Reduction ◽  
Fuzzy Relation ◽  
Discernibility Matrix ◽  
Intuitionistic Fuzzy ◽  
Approximation Operators

We aim to investigate intuitionistic fuzzy ordered information systems. The concept of intuitionistic fuzzy ordered information systems is proposed firstly by introducing an intuitionistic fuzzy relation to ordered information systems. And a ranking approach for all objects is constructed in this system. In order to simplify knowledge representation, it is necessary to reduce some dispensable attributes in the system. Theories of rough set are investigated in intuitionistic fuzzy ordered information systems by defining two approximation operators. Moreover, judgement theorems and methods of attribute reduction are discussed based on discernibility matrix in the systems, and an illustrative example is employed to show its validity. These results will be helpful for decisionmaking analysis in intuitionistic fuzzy ordered information systems.

INTUITIONISTIC FUZZY ORDERED INFORMATION SYSTEM

2013 ◽  
Vol 21 (03) ◽  
pp. 367-390 ◽  
Author(s):  
WEIHUA XU ◽  
YUFENG LIU ◽  
TONGJUN LI
Keyword(s):  
Decision Making ◽  
Information System ◽  
Information Systems ◽  
Rough Set ◽  
Fuzzy Relation ◽  
Intuitionistic Fuzzy ◽  
Approximation Operators

In this paper, we aim to study intuitionistic fuzzy ordered information systems. Firstly, the concept of intuitionistic fuzzy ordered information systems is proposed by introducing an intuitionistic fuzzy relation to ordered information systems. Meanwhile, two approximation operators are defined and a rough set approach is established in intuitionistic fuzzy ordered information systems. Secondly, a ranking approach for all objects is constructed in this system. Thirdly, approximation reduction is addressed in intuitionistic fuzzy ordered decision information system. These results will be helpful for decision-making analysis in intuitionistic fuzzy ordered information systems.

Generalized dynamic attribute reduction based on similarity relation of intuitionistic fuzzy rough set

2020 ◽  
Vol 39 (5) ◽  
pp. 7107-7122
Author(s):  
Zhang Chuanchao
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Rough Set ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Similarity Relation ◽  
Fuzzy Rough Set ◽  
Data Set ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Rough Sets

In view of the characteristics with big data, high feature dimension, and dynamic for a large-scale intuitionistic fuzzy information systems, this paper integrates intuitionistic fuzzy rough sets and generalized dynamic sampling theory, proposes a generalized attribute reduction algorithm based on similarity relation of intuitionistic fuzzy rough sets and dynamic reduction. It uses dynamic reduction sampling theory to divide a big data set into small data sets and relative positive domain cardinality instead of dependency degree as decision-making condition, and obtains reduction attributes of big intuitionistic fuzzy decision information systems, and achieves the goal of extracting key features and fault diagnosis. The innovation of this paper is that it integrates generalized dynamic reduction and intuitionistic fuzzy rough set, and solves the problem of big data set which cannot be solved by intuitionistic fuzzy rough set. Taking an actual data as an example, the scientificity, rationality and effectiveness of the algorithm are verified from the aspects of stability, diagnostic accuracy, optimization ability and time complexity. Compared with similar algorithms, the advantages of the proposed algorithm for big data processing are confirmed.

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.

ON ATTRIBUTE REDUCTION WITH INTUITIONISTIC FUZZY ROUGH SETS

2012 ◽  
Vol 20 (01) ◽  
pp. 59-76 ◽  
Author(s):  
ZHIMING ZHANG ◽  
JINGFENG TIAN
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Theoretical Foundation ◽  
Attribute Reduction ◽  
Upper Approximation ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Rough Sets ◽  
Axiomatic Approaches

Intuitionistic fuzzy (IF) rough sets are the generalization of traditional rough sets obtained by combining the IF set theory and the rough set theory. The existing research on IF rough sets mainly concentrates on the establishment of lower and upper approximation operators using constructive and axiomatic approaches. Less effort has been put on the attribute reduction of databases based on IF rough sets. This paper systematically studies attribute reduction based on IF rough sets. Firstly, attribute reduction with traditional rough sets and some concepts of IF rough sets are reviewed. Then, we introduce some concepts and theorems of attribute reduction with IF rough sets, and completely investigate the structure of attribute reduction. Employing the discernibility matrix approach, an algorithm to find all attribute reductions is also presented. Finally, an example is proposed to illustrate our idea and method. Altogether, these findings lay a solid theoretical foundation for attribute reduction based on IF rough sets.

Attribute Reduction Using Bayesian Decision Theoretic Rough Set Models

2014 ◽  
Vol 1 (1) ◽  
pp. 15-31 ◽  
Author(s):  
Sharmistha Bhattacharya Halder ◽  
Kalyani Debnath
Keyword(s):  
Boolean Function ◽  
Rough Set ◽  
Bayesian Method ◽  
Probabilistic Approach ◽  
Attribute Reduction ◽  
Bayesian Decision ◽  
Discernibility Matrix ◽  
Variable Precision ◽  
Precision Method ◽  
Positive Boundary

Bayesian Decision theoretic rough set has been invented by the author. In this paper the attribute reduction by the aid of Bayesian decision theoretic rough set has been studied. Lot of other methods are there for attribute reduction such as Variable precision method, probabilistic approach, Bayesian method, Pawlaks rough set method using Boolean function. But with the help of some example it is shown that Bayesian decision theoretic rough set model gives better result than other method. Lastly an example of HIV /AIDS is taken and attribute reduction is done by this new method and various other method. It is shown that this method gives better result than the previously defined methods. By this method the authors get only the reduced attribute age which is the best significant attribute. Though in Pawlak model age sex or age living status are the reduced attribute and variable precision method fails to work here. In this paper attribute reduction is done by the help of discernibility matrix after determining the positive, boundary and negative region. This model is a hybrid model of Bayesian rough set model and decision theory. So this technique gives better result than Bayesian method and decision theoretic rough set method.

Sign in / Sign up

Export Citation Format

Share Document