Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation

With respect to multiple attribute group decision making problems in which the attribute weights and the expert weights take the form of real numbers and the attribute values take the form of interval-valued uncertain linguistic variable. In this paper, we introduce the idea of variable precision into the incomplete interval-valued fuzzy information system and propose the theory of variable precision rough sets over incomplete interval-valued fuzzy information systems. Then, we give the properties of rough approximation operators and study the knowledge discovery and attribute reduction in the incomplete interval-valued fuzzy information system under the condition that a certain degree of misclassification rate is allowed to exist. Furthermore, a decision rule and decision model are given. Finally, an illustrative example is given and compared with the existing methods, the practicability and effectiveness of this method are further verified.

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Modelling of gene signal attribute reduction based on neighbourhood granulation and rough approximation

International Journal of Modelling Identification and Control ◽

10.1504/ijmic.2019.10019338 ◽

2019 ◽

Vol 31 (2) ◽

pp. 161

Author(s):

Tao Hou ◽

Jing Bai ◽

Jian Xue ◽

Fu Liu

Keyword(s):

Attribute Reduction ◽

Rough Approximation

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Attribute Reduction of Service Quality Based on Factor Analysis and Neighborhood Model

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.4.201 ◽

2012 ◽

Vol 4 ◽

pp. 201-207

Author(s):

Lin Chen ◽

Xian Hu

Keyword(s):

Factor Analysis ◽

Service Quality ◽

Empirical Research ◽

Quality Evaluation ◽

Attribute Reduction ◽

Research Data ◽

Total Correlation ◽

Rough Approximation ◽

Service Quality Evaluation ◽

Service Attribute

How to choose the attributes of service quality have become the foundation and the key for researches of service quality. In connection with the current situation and characteristics of the existing e-commerce service quality evaluation, we analyze the advantages and shortcomings of the widely used way, which is combining item-to-total correlation and factor analysis to reduce our service attribute scale. Then a method of neighborhood granulation and rough approximation for numerical attribute reduction is proposed. With the comparison of the two methods through specific examples of empirical research data, the validity and superiority of the application of neighborhood model for numerical attribute reduction are verified.

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Attribute Reduction Based on Rough Approximation Set in Algebra and Information Views

IEEE Access ◽

10.1109/access.2016.2600252 ◽

2016 ◽

Vol 4 ◽

pp. 5399-5407 ◽

Cited By ~ 4

Author(s):

Qinghua Zhang ◽

Jingjing Yang ◽

Longyang Yao

Keyword(s):

Attribute Reduction ◽

Rough Approximation ◽

Approximation Set

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Combined Accelerator for Attribute Reduction: A Sample Perspective

Mathematical Problems in Engineering ◽

10.1155/2020/2350627 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Yan Chen ◽

Jingjing Song ◽

Keyu Liu ◽

Yaojin Lin ◽

Xibei Yang

Keyword(s):

Rough Set ◽

Attribute Reduction ◽

Data Sets ◽

Multiple Views ◽

Time Consumption ◽

Elapsed Time ◽

Rough Approximation ◽

Neighborhood Rough Set ◽

Speed Up ◽

Neighborhood Relation

In the field of neighborhood rough set, attribute reduction is considered as a key topic. Neighborhood relation and rough approximation play crucial roles in the process of obtaining the reduct. Presently, many strategies have been proposed to accelerate such process from the viewpoint of samples. However, these methods speed up the process of obtaining the reduct only from binary relation or rough approximation, and then the obtained results in time consumption may not be fully improved. To fill such a gap, a combined acceleration strategy based on compressing the scanning space of both neighborhood and lower approximation is proposed, which aims to further reduce the time consumption of obtaining the reduct. In addition, 15 UCI data sets have been selected, and the experimental results show us the following: (1) our proposed approach significantly reduces the elapsed time of obtaining the reduct; (2) compared with previous approaches, our combined acceleration strategy will not change the result of the reduct. This research suggests a new trend of attribute reduction using the multiple views.

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Grey variable dual precision rough set model and its application

Grey Systems Theory and Application ◽

10.1108/gs-11-2020-0141 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Junliang Du ◽

Sifeng Liu ◽

Yong Liu

Keyword(s):

Decision Making ◽

Decision Rule ◽

Rough Set ◽

Attribute Reduction ◽

Degree Of Approximation ◽

Membership Degree ◽

Content Type ◽

Variable Precision ◽

Rough Approximation ◽

Variable Precision Rough Set

PurposeThe purpose of this paper is to advance a novel grey variable dual precision rough set model for grey concept.Design/methodology/approachTo obtain the approximation of a grey object, the authors first define the concepts of grey rough membership degree and grey degree of approximation on the basic thinking logic of variable precision rough set. Based on grey rough membership degree and grey degree of approximation, the authors proposed a grey variable dual precision rough set model. It uses a clear knowledge concept to approximate a grey concept, and the output result is also a clear concept.FindingsThe result demonstrates that the proposed model may be closer to the actual decision-making situation, can effectively improve the rationality and scientificity of the approximation and reduce the risk of decision-making. It can effectively achieve the whitenization of grey objects. The model can be degenerated to traditional variable precision rough fuzzy set model, variable precision rough set model and classic Pawlak rough set, when some specific conditions are met.Practical implicationsThe method exposed in the paper can be used to solve multi-criteria decision problems with grey decision objects and provide a decision rule. It can also help us better realize knowledge discovery and attribute reduction. It can effectively achieve the whitenization of grey object.Originality/valueThis method proposed in this paper implements a rough approximation of grey decision object and obtains low-risk probabilistic decision rule. It can effectively achieve a certain degree of whitenization of some grey objects.

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