New approaches to intuitionistic fuzzy-rough attribute reduction

2018 ◽  
Vol 34 (5) ◽  
pp. 3385-3394 ◽  
Author(s):  
Anoop Kumar Tiwari ◽  
Shivam Shreevastava ◽  
K.K. Shukla ◽  
Karthikeyan Subbiah
Keyword(s):  
Attribute Reduction ◽  
Intuitionistic Fuzzy ◽  
New Approaches

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.

scholarly journals Attribute Reduction of Intuitionistic Fuzzy Concept Lattices

2015 ◽  
Vol 713-715 ◽  
pp. 1649-1654 ◽  
Author(s):  
Hong Wang ◽  
Hong Juan Zhang
Keyword(s):  
Information Systems ◽  
Attribute Reduction ◽  
Dominance Relation ◽  
Formal Concept ◽  
Fuzzy Information ◽  
Concept Lattices ◽  
Fuzzy Concept ◽  
Intuitionistic Fuzzy

In this paper, we turn intuitionistic fuzzy information systems into 0-1 formal contexts by using dominance relation. A pair of operators is defined to get the formal concept lattices in the intuitionistic fuzzy information systems. Furthermore, some properties and attribute reduction based on discernibility matrices is investigated.

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.

A Novel Filter-Wrapper Algorithm on Intuitionistic Fuzzy Set for Attribute Reduction From Decision Tables

2021 ◽  
Vol 17 (4) ◽  
pp. 67-100
Author(s):  
Thang Truong Nguyen ◽  
Nguyen Long Giang ◽  
Dai Thanh Tran ◽  
Trung Tuan Nguyen ◽  
Huy Quang Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Fuzzy Set ◽  
Attribute Reduction ◽  
Intuitionistic Fuzzy Set ◽  
Data Element ◽  
Fuzzy Distance ◽  
Intuitionistic Fuzzy ◽  
Decision Tables

Attribute reduction from decision tables is one of the crucial topics in data mining. This problem belongs to NP-hard and many approximation algorithms based on the filter or the filter-wrapper approaches have been designed to find the reducts. Intuitionistic fuzzy set (IFS) has been regarded as the effective tool to deal with such the problem by adding two degrees, namely the membership and non-membership for each data element. The separation of attributes in the view of two counterparts as in the IFS set would increase the quality of classification and reduce the reducts. From this motivation, this paper proposes a new filter-wrapper algorithm based on the IFS for attribute reduction from decision tables. The contributions include a new instituitionistics fuzzy distance between partitions accompanied with theoretical analysis. The filter-wrapper algorithm is designed based on that distance with the new stopping condition based on the concept of delta-equality. Experiments are conducted on the benchmark UCI machine learning repository datasets.

scholarly journals Attribute Reduction in Intuitionistic Fuzzy Concept Lattices

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jinzhong Pang ◽  
Xiaoyan Zhang ◽  
Weihua Xu
Keyword(s):  
Knowledge Discovery ◽  
Lattice Theory ◽  
Fuzzy Theory ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Discernibility Matrix ◽  
Fuzzy Concept Lattice ◽  
Fuzzy Concept ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Theory

As an effective tool for knowledge discovery, concept lattice has been successfully applied to various fields. And one of the key problems of knowledge discovery is attribute reduction. In order to understand the problems better, the attribute reduction is necessary to perfect the theory as well as expand application of concept lattice. This paper introduces the intuitionistic fuzzy theory into the concept lattice theory and proposes a kind of intuitionistic fuzzy concept lattice. Then, an approach to attribute reduction based on the discernibility matrix is proposed and investigated, which makes the discovery of implicit knowledge easier and the representation simpler in data; furthermore, the theory of concept lattice is perfected. The theory of intuitionistic fuzzy concept lattice is useful and meaningful in view of the complexity and fuzziness of information in real world, and the potential value of dealing with information is expected in the future.

scholarly journals The Method of Data Analysis in Intuitionistic Fuzzy Generalized Consistent Decision Formal Context

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 262
Author(s):  
Binbin Sang ◽  
Binghan Long ◽  
Jinzhong Pang ◽  
Weihua Xu
Keyword(s):  
Data Analysis ◽  
Lattice Theory ◽  
Fuzzy Theory ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Formal Context ◽  
Intuitionistic Fuzzy ◽  
Decision Formal Context ◽  
Intuitionistic Fuzzy Theory

Concept lattice has been successfully applied to various fields as an effective tool for data analysis and knowledge discovery, with attribute reduction being the key problem. This paper combines the intuitionistic fuzzy theory with the concept lattice theory and proposes one kind of concept lattice in intuitionistic fuzzy generalized consistent decision formal context. Furthermore, an approach to attribute a reduction in the discernibility matrix is proposed and investigated, making the discovery of implicit knowledge easier and the representation simpler in the data system and perfecting the theory of concept lattice. Moreover, this paper studies, in detail, the algorithms and case study of data analysis in the intuitionistic fuzzy generalized consistent decision formal context. The potential value of the method to deal with information discussed in this paper, especially the value of forecasting and decision-making, is expected in future.

Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction

2018 ◽  
Vol 101 ◽  
pp. 205-212 ◽  
Author(s):  
Anoop Kumar Tiwari ◽  
Shivam Shreevastava ◽  
Tanmoy Som ◽  
K.K. Shukla
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy
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