scholarly journals Multi-Label Attribute Reduction Based on Variable Precision Fuzzy Neighborhood Rough Set

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 133565-133576
Author(s):  
Panpan Chen ◽  
Menglei Lin ◽  
Jinghua Liu
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Variable Precision ◽  
Neighborhood Rough Set

A New Rough Set Model and its Application

2014 ◽  
Vol 599-601 ◽  
pp. 1350-1356
Author(s):  
Ming Ming Jia ◽  
Hai Qin Qin ◽  
Yong Qi Wang ◽  
Ke Jun Xu
Keyword(s):  
Rough Set ◽  
Discrete Variables ◽  
Water Pump ◽  
Variable Precision ◽  
Engineering Problems ◽  
Neighborhood Rough Set ◽  
Variable Precision Rough Set ◽  
Robust To Noise

A new neighborhood variable precision rough set modal is presented in this paper. The modal possesses the characteristics of neighborhood rough set and variable precision rough set, so it can overcome shortcomings of classic rough set which only be fit for discrete variables and sensitive to noise. Based on giving the definitions of approximate reduction, lower and upper approximate reduction, lower and upper distribution reduction, two kinds of algorithms to confirm lower and upper distribution reduction were advanced. The modal was applied to diagnose one frequency modulated water pump vibration faults. The result shows the modal is more suitable to engineering problems, because it can not only deal with continues variables but also be robust to noise.

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.

Attribute reduction based on max-decision neighborhood rough set model

2018 ◽  
Vol 151 ◽  
pp. 16-23 ◽  
Author(s):  
Xiaodong Fan ◽  
Weida Zhao ◽  
Changzhong Wang ◽  
Yang Huang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Neighborhood Rough Set

The Research of Constructing Rough Concept Lattices Model

2011 ◽  
Vol 63-64 ◽  
pp. 664-667
Author(s):  
Hong Sheng Xu ◽  
Ting Zhong Wang
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Concept Lattice ◽  
Attribute Reduction ◽  
Formal Concept ◽  
Concept Lattices ◽  
Variable Precision ◽  
Whole Process ◽  
Variable Precision Rough Set ◽  
Lattice Construction

Formal concept lattices and rough set theory are two kinds of complementary mathematical tools for data analysis and data processing. The algorithm of concept lattice reduction based on variable precision rough set is proposed by combining the algorithms of β-upper and lower distribution reduction in variable precision rough set. The traditional algorithms aboutβvalue select algorithm, attribute reduction based on discernibility matrix and extraction rule in VPRS are discussed, there are defects in these traditional algorithms which are improved. Finally, the generation system of concept lattice based on variable precision rough set is designed to verify the validity of the improved algorithm and a case demonstrates the whole process of concept lattice construction.

Sign in / Sign up

Export Citation Format

Share Document