scholarly journals Double-Quantitative Generalized Multi-Granulation Set-Pair Dominance Rough Sets in Incomplete Ordered Information System

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 133
Author(s):  
Zhan-ao Xue ◽  
Min Zhang ◽  
Yong-xiang Li ◽  
Li-ping Zhao ◽  
Bing-xin Sun
Keyword(s):  
Decision Making ◽  
Information System ◽  
Rough Sets ◽  
Decision Rules ◽  
Rough Sets Theory ◽  
Quantification Method ◽  
Variable Precision ◽  
Double Quantification ◽  
Sets Theory ◽  
Variable Precision Rough Sets

Since the rough sets theory based on the double quantification method was proposed, it has attracted wide attention in decision-making. This paper studies the decision-making approach in Incomplete Ordered Information System (IOIS). Firstly, to better extract the effective information in IOIS, combined with the advantages of set-pair dominance relation and generalized multi-granulation, the generalized multi-granulation set-pair dominance variable precision rough sets (GM-SPD-VPRS) and the generalized multi-granulation set-pair dominance graded rough sets (GM-SPD-GRS) are proposed. Moreover, we discuss their related properties. Secondly, considering the GM-SPD-VPRS and the GM-SPD-GRS describe information from relative view and absolute view, respectively, we further combine the two rough sets to obtain six double-quantitative generalized multi-granulation set-pair dominance rough sets (GM-SPD-RS) models. Among them, the first two models fuse the approximation operators of two rough sets, and investigate the extreme cases of optimistic and pessimistic. The last four models combine the two rough sets by the logical disjunction operator and the logical conjunction operator. Then, we discuss relevant properties and derive the corresponding decision rules. According to the decision rules, an associated algorithm is constructed for one of the models to calculate the rough regions. Finally, we validate the effectiveness of these models with a medical example. The results indicate that the model is effective for dealing with practical problems.

ROUGH SETS AND GENETIC ALGORITHMS IN LEARNING CELLULAR NEURAL NETWORKS CLONING TEMPLATE FOR DECISION MAKING SYSTEM

2004 ◽  
Vol 14 (01) ◽  
pp. 57-68 ◽  
Author(s):  
ELSAYED RADWAN ◽  
EIICHIRO TAZAKI
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Rough Sets ◽  
Decision Rules ◽  
Cellular Neural Networks ◽  
Minimal Set ◽  
Rough Sets Theory ◽  
Decision Making System ◽  
Sets Theory

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

An Improved Variable Precision Rough Sets Weighting Model for Healthy Housing

2013 ◽  
Vol 462-463 ◽  
pp. 316-320
Author(s):  
Li Tan ◽  
Xiao Yao Wang ◽  
Chong Chong Yu ◽  
Gui Long Xie
Keyword(s):  
Rough Sets ◽  
Error Rates ◽  
Living Environment ◽  
Rough Sets Theory ◽  
The Core ◽  
Variable Precision ◽  
The Index System ◽  
Sets Theory ◽  
Healthy Housing ◽  
Variable Precision Rough Sets

Classical rough sets method makes knowledge absence in the key domain and affects the comprehensiveness of the index system after simplification, so an improved variable precision rough sets weighting model is proposed on the basis of variable precision rough sets theory. In the model, attributes are divided into first-class core attributes and second-class attributes, and core attributes are still calculated by the importance of the attributes in rough sets theory. The importance of second-class attribute is μ times of the core attributes’ minimum importance degree. Then normalize the importance degrees and convert them to weights. In the evaluation of the health of sound living environment, the model we proposed has lower error rates compared to other methods and the evaluation results demonstrate to be valid.

Compound-Fault Diagnosis of Bearing Based on Order Tracking Wavelet Packet and Rough Sets

2011 ◽  
Vol 130-134 ◽  
pp. 1681-1685 ◽  
Author(s):  
Guang Tian ◽  
Hao Tian ◽  
Guang Sheng Liu ◽  
Jin Hui Zhao ◽  
Li Ping Luo
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Wavelet Packet ◽  
Decision Rules ◽  
Vibration Signals ◽  
Rough Sets Theory ◽  
Equal Angle ◽  
Order Tracking ◽  
Start Up ◽  
Sets Theory

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field, then a new method of compound-fault diagnosis was presented. The vibration signals at start-up in the gearbox are non-stationary signals, and traditional ways of diagnosis have low precision. Order tracking and wavelet packet and rough sets theory are introduced in the compound-fault diagnosis of bearing. First, the vibration signals at start-up were resampled using computer order tracking arithmetic and equal angle distributed vibration signals were obtained, and wavelet packet has been used for equal angle distributed vibration signals decomposition and reconstruction. Then, energy distribution of every frequency band can be calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound-fault example proves that the proposed method has high validity and more amplitude appliance foreground.

Two Types of Decision Rules Based on Variable Precision Rough Sets

2012 ◽  
Vol 4 (14) ◽  
pp. 270-278
Author(s):  
Weiyan Xu ◽  
Jiashang Jiang ◽  
Ming Zhang
Keyword(s):  
Rough Sets ◽  
Decision Rules ◽  
Variable Precision ◽  
Variable Precision Rough Sets

DECISION MAKING USING HYBRID ROUGH SETS AND NEURAL NETWORKS

2002 ◽  
Vol 12 (06) ◽  
pp. 435-446 ◽  
Author(s):  
YASSER HASSAN ◽  
EIICHIRO TAZAKI ◽  
SHIN EGAWA ◽  
KAZUHO SUYAMA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Rough Sets ◽  
Decision Problem ◽  
Combined System ◽  
Network Systems ◽  
Rough Sets Theory ◽  
Sets Theory

A methodology for using rough sets theory for preference modeling in decision problem is presented in this paper. We will introduce a new method where neural network systems and rough sets theory are completely integrated into a hybrid system and are used cooperatively for decision and classification support. At the first glance, the two methods we discuss have not much in common. But, in spite of the differences between them, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.

Accident Causation Factor Analysis of Traffic Accidents using Rough Relational Analysis

2016 ◽  
Vol 3 (3) ◽  
pp. 60-71 ◽  
Author(s):  
Caner Erden ◽  
Numan Çelebi
Keyword(s):  
Big Data ◽  
Rough Sets ◽  
Traffic Accidents ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Decision Makers ◽  
Rough Sets Theory ◽  
Relational Analysis ◽  
Accident Causation ◽  
Sets Theory

The aim of this study is to show that the decision rules generated from Rough Sets Theory can be used for a new relational analysis. Rough Sets Theory generally works with small datasets more than big data. If we can deal with the decision rules and its complexities, it is still possible to analyze big data with Rough Set Theory. That is why in this study the authors offer a statistical method to overdue problems which belongs to big data. According statistical methods, a lots of decision rules generated from rough sets theory become useful information. Using a real case data on the traffic accident which were taken place in USA in 2013, this paper finds the relationships between accident causation factors which may be referred to decision makers in the field of traffic.

An Exposition of Feature Selection and Variable Precision Rough Set Analysis

2010 ◽  
pp. 193-213
Author(s):  
Malcolm J. Beynon ◽  
Benjamin Griffiths
Keyword(s):  
Soft Computing ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Credit Ratings ◽  
Decision Rules ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Variable Precision Rough Sets

This chapter considers, and elucidates, the general methodology of rough set theory (RST), a nascent approach to rule based classification associated with soft computing. There are two parts of the elucidation undertaken in this chapter, firstly the levels of possible pre-processing necessary when undertaking an RST based analysis, and secondly the presentation of an analysis using variable precision rough sets (VPRS), a development on the original RST that allows for misclassification to exist in the constructed “if … then …” decision rules. Throughout the chapter, bespoke software underpins the pre-processing and VPRS analysis undertaken, including screenshots of its output. The problem of US bank credit ratings allows the pertinent demonstration of the soft computing approaches described throughout.

A New Improved Attribute Weight Algorithm Based on Rough Sets Theory for One Command Information System

2014 ◽  
Vol 989-994 ◽  
pp. 2029-2032
Author(s):  
Su Min Yang ◽  
Ying Min Yan ◽  
Kai Wang ◽  
Zhi Ying Xie
Keyword(s):  
Information System ◽  
Rough Sets ◽  
Policy Decision ◽  
Actual Situation ◽  
Attribute Weighting ◽  
Rough Sets Theory ◽  
Attribute Weight ◽  
Subjective Weight ◽  
The Status ◽  
Sets Theory

There are various evaluation indicators in command information system. It is important to determine the weight of each indicator because it has a direct impact on the final result for evaluation and decision making. The reasonable and accurate attribute weight is helpful to ascertain the status or effect on the policy decision. With analyzing the deficiency of attribute weighting algorithms based on the rough sets theory, the new attribute weight algorithm is proposed in the paper. The proposed algorithm considers objective weight and subjective weight. The objective weight includes three factors, named as the importance of the attribute itself, the increment of mutual information, and its own information entropy. The subjective weight is obtained by the experts with prior knowledge in the field. Experiment results prove that the new method not only overcomes the deficiency of the existing weight methods, but also is more in line with the actual situation.

scholarly journals PARAMETRIC ANALYSIS OF A GRINDING PROCESS USING THE ROUGH SETS THEORY

2020 ◽  
Vol 18 (1) ◽  
pp. 091
Author(s):  
Subham Agarwal ◽  
Shruti Sudhakar Dandge ◽  
Shankar Chakraborty
Keyword(s):  
Rough Sets ◽  
Decision Rules ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Rough Sets Theory ◽  
Related Data ◽  
Grinding Parameters ◽  
Sets Theory

With continuous automation of the manufacturing industries and the development of advanced data acquisition systems, a huge volume of manufacturing-related data is now available which can be effectively mined to extract valuable knowledge and unfold the hidden patterns. In this paper, a data mining tool, in the form of the rough sets theory, is applied to a grinding process to investigate the effects of its various input parameters on the responses. Rotational speed of the grinding wheel, depth of cut and type of the cutting fluid are grinding parameters, and average surface roughness, amplitude of vibration and grinding ratio are the responses. The best parametric settings of the grinding parameters are also derived to control the quality characteristics of the ground components. The developed decision rules are quite easy to understand and can truly predict the response values at varying combinations of the considered grinding parameters.

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