Rough approximation of a fuzzy concept on a hybrid attribute information system and its uncertainty measure

2014 ◽  
Vol 284 ◽  
pp. 60-80 ◽  
Author(s):  
Bingzhen Sun ◽  
Weimin Ma ◽  
Degang Chen
Keyword(s):  
Information System ◽  
Uncertainty Measure ◽  
Fuzzy Concept ◽  
Rough Approximation ◽  
Attribute Information

Uncertainty Measure Based on Evidence Theory

2013 ◽  
Vol 329 ◽  
pp. 344-348
Author(s):  
Shao Pu Zhang ◽  
Tao Feng
Keyword(s):  
Information System ◽  
Information Fusion ◽  
Rough Set Theory ◽  
Evidence Theory ◽  
Belief Function ◽  
Mass Function ◽  
Uncertainty Measure ◽  
Rough Approximation ◽  
Uncertainty Information ◽  
Uncertainty Method

Evidence theory is an effective method to deal with uncertainty information. And uncertainty measure is to reflect the uncertainty of an information system. Thus we want to merge evidence theory with uncertainty method in order to measure the roughness of a rough approximation space. This paper discusses the information fusion and uncertainty measure based on rough set theory. First, we propose a new method of information fusion based on the Bayse function, and define a pair of belief function and plausibility function using the fused mass function in an information system. Then we construct entropy for every decision class to measure the roughness of every decision class, and entropy for decision information system to measure the consistence of decision table.

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT ALERGI PADA ANAK BERBASIS WEB DENGAN METODE FORWARD CHAINING DI KOTA BATAM

2017 ◽  
Vol 3 (2) ◽  
pp. 197-205
Author(s):  
Nanda Luthan
Keyword(s):  
Information Technology ◽  
Information System ◽  
Information Systems ◽  
Patient Information ◽  
Business Processes ◽  
Forward Chaining ◽  
Important Requirement ◽  
Attribute Information ◽  
Need For Information

The need for information systems is a very important requirement for running business processes. As the development of technology is very necessary when an information technology really supports the information systems required form. Kano method can be used to determine the need for patient information systems. With the process of the Kano method used is based on the need for information systems. because the method aims to mengatagorikan canoe will need attribute information systems. ignorance of the service attributes can cause a negative for the company services. It therefore requires an information system that can meet the needs of patient information so users of patient information is needed to support the company's performance

On variable-precision-based rough set approach to incomplete interval-valued fuzzy information systems and its applications

2021 ◽  
Vol 40 (1) ◽  
pp. 463-475
Author(s):  
Juan Li ◽  
Yabin Shao ◽  
Xiaoding Qi
Keyword(s):  
Information System ◽  
Information Systems ◽  
Attribute Reduction ◽  
Misclassification Rate ◽  
Fuzzy Information ◽  
Attribute Weights ◽  
Variable Precision ◽  
Multiple Attribute ◽  
Rough Approximation ◽  
Interval Valued

 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.

The Construction of Fuzzy Concept Lattices Based on (θ, σ)-Fuzzy Rough Approximation Operators

2011 ◽  
Vol 111 (1) ◽  
pp. 33-45 ◽  
Author(s):  
Yanqing Yao ◽  
Jusheng Mi ◽  
Zhoujun Li ◽  
Bin Xie
Keyword(s):  
Concept Lattices ◽  
Fuzzy Concept ◽  
Approximation Operators ◽  
Rough Approximation

scholarly journals Library Space Information Model Based on GIS — A Case Study of Shanghai Jiao Tong University

2018 ◽  
Vol 37 (3) ◽  
pp. 99-110 ◽  
Author(s):  
Yaqi Shen
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
Spatial Information ◽  
Information Model ◽  
Geographical Information ◽  
Reading Room ◽  
Attribute Information ◽  
Special Collection ◽  
Query System

In this paper, a library-space information model (LSIM) based on a geographical information system (GIS) was built to visually show the bookshelf location of each book through the display interface of various terminals. Taking Shanghai Jiao Tong University library as an example, both spatial information and attribute information were integrated into the model. In the spatial information, the reading room layout, bookshelves, reference desks, and so on were constructed with different attributes. The bookshelf layer was the key attribute of the bookshelves, and each book was linked to one bookshelf layer. Through the field of bookshelf layer, the book in the query system can be connected with the bookshelf-layer information of the LSIM. With the help of this model, readers can search books visually in the query system and find the books’ positions accurately. It can also be used in the inquiry of special-collection resources. Additionally, librarians can use this model to analyze books’ circulation status, and books with similar subjects that are frequently circulated can be recommended to readers. The library’s permanent assets (chairs, tables, etc.) could be managed visually in the model. This paper used GIS as a tool to solve the problem of accurate positioning, simultaneously providing better services for readers and realizing visual management of books for librarians.

Similarity measure for multi-granularity rough approximations of vague sets

2021 ◽  
Vol 40 (1) ◽  
pp. 1609-1621
Author(s):  
Jie Yang ◽  
Wei Zhou ◽  
Shuai Li
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Target Concept ◽  
Uncertainty Measure ◽  
Approximation Spaces ◽  
Vague Sets ◽  
Rough Approximation ◽  
Vague Concept

Vague sets are a further extension of fuzzy sets. In rough set theory, target concept can be characterized by different rough approximation spaces when it is a vague concept. The uncertainty measure of vague sets in rough approximation spaces is an important issue. If the uncertainty measure is not accurate enough, different rough approximation spaces of a vague concept may possess the same result, which makes it impossible to distinguish these approximation spaces for charactering a vague concept strictly. In this paper, this problem will be solved from the perspective of similarity. Firstly, based on the similarity between vague information granules(VIGs), we proposed an uncertainty measure with strong distinguishing ability called rough vague similarity (RVS). Furthermore, by studying the multi-granularity rough approximations of a vague concept, we reveal the change rules of RVS with the changing granularities and conclude that the RVS between any two rough approximation spaces can degenerate to granularity measure and information measure. Finally, a case study and related experiments are listed to verify that RVS possesses a better performance for reflecting differences among rough approximation spaces for describing a vague concept.

Online Flood Information System

2014 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Xiannian Chen ◽  
Xinyue Ye ◽  
Michael C. Carroll ◽  
Yingru Li
Keyword(s):  
Information System ◽  
Web Service ◽  
Spatial Patterns ◽  
Data Set ◽  
Spatial Intelligence ◽  
Web Map ◽  
Attribute Information ◽  
Flood Disasters ◽  
Inquiry System ◽  
User Friendly

This paper implements a cyber-platform which visualizes and analyzes spatial patterns of flooding with a user-oriented spatial intelligence. The paper is organized from three perspectives: first, why representation and modeling of flooding data set is vital; second, how the design of flooding analysis involves spatial intelligence; third, why flooding analysis should be integrated into Cyber-infrastructure. The flood is one of the most common and devastative disasters. Flood disasters bring huge damages to the affected communities and beyond. Hence, a fast and effective flood information inquiry system is critical to reduce the loss. REST-based Web Service illustrates its great advantages in web map re-rendering, attribute information retrieving, and advanced GIS functions. This research introduces how to use REST-based Web Service to build a user-friendly online flood information inquiry system.

Construction Method of Fuzzy Attribute Information System Based on DEA and Attribute Reduction

2011 ◽  
Vol 50-51 ◽  
pp. 130-134
Author(s):  
Yan Kun Li
Keyword(s):  
Information System ◽  
Data Envelopment Analysis ◽  
Index System ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Construction Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Data Envelopment ◽  
Attribute Information

For the subjectivity of attribute value in the fuzzy attribute information system, this paper proposes a construction method of fuzzy attribute information system based on DEA (Data Envelopment Analysis). Then an attribute reduction method based on the dependability degree of attribute is given. At last, the proposed method is implemented successfully in the optimization of evaluation index system.

scholarly journals Modified Uncertainty Measure of Rough Fuzzy Sets from the Perspective of Fuzzy Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Yang ◽  
Taihua Xu ◽  
Fan Zhao
Keyword(s):  
Fuzzy Sets ◽  
Rough Sets ◽  
Boundary Region ◽  
Uncertainty Measure ◽  
Approximation Spaces ◽  
Rough Sets Theory ◽  
Fuzzy Distance ◽  
Rough Approximation ◽  
Sets Theory

As an extension of Pawlak’s rough sets, rough fuzzy sets are proposed to deal with fuzzy target concept. As we know, the uncertainty of Pawlak’s rough sets is rooted in the objects contained in the boundary region, while the uncertainty of rough fuzzy sets comes from three regions (positive region, boundary region, and negative region). In addition, in the view of traditional uncertainty measures, the two rough approximation spaces with the same uncertainty are not necessarily equivalent, and they cannot be distinguished. In this paper, firstly, a fuzziness-based uncertainty measure is proposed. Meanwhile, the essence of the uncertainty for rough fuzzy sets and its three regions in a hierarchical granular structure is revealed. Then, from the perspective of fuzzy distance, we introduce a modified uncertainty measure based on the fuzziness-based uncertainty measure and present that our method not only is strictly monotonic with finer approximation spaces, but also can distinguish the two rough approximation spaces with the same uncertainty. Finally, a case study is introduced to demonstrate that the modified uncertainty measure is more suitable for evaluating the significance of attributes. These works are useful for further study on rough sets theory and promote the development of uncertain artificial intelligence.

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