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.

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.

Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery

2000 ◽  
Vol 4 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Ilona Jagielska ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Selection ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Real World ◽  
Rough Sets ◽  
Network Models ◽  
Feature Subset Selection ◽  
Neural Network Models

An important task in knowledge discovery is feature selection. This paper describes a practical approach to feature subset selection proposed as part of a hybrid rough sets/neural network framework for knowledge discovery for decision support. In this framework neural networks and rough sets are combined and used cooperatively during the system life cycle. The reason for combining rough sets with neural networks in the proposed framework is twofold. Firstly, rough sets based systems provide domain knowledge expressed in the form of If-then rules as well as tools for data analysis. Secondly, rough sets are used in this framework in the task of feature selection for neural network models. This paper examines the feature selection aspect of the framework. An empirical study that tested the approach on artificial datasets and real-world datasets was carried out. Experimental results indicate that the proposed approach can improve the performance of neural network models. The framework was also applied in the development of a real-world decision support system. The experience with this application has shown that the approach can support the users in the task of feature selection.

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.

Study on Fault Diagnosis of Power Transformer with Reduction Method of Attribute Significance

2014 ◽  
Vol 1049-1050 ◽  
pp. 665-668
Author(s):  
Hong Li Lv
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Sets ◽  
Power Transformer ◽  
Reduction Algorithm ◽  
Rough Sets Theory ◽  
Fault Diagnosis System ◽  
Transformer Fault ◽  
Sets Theory ◽  
Quality Diagnosis

This paper studies the power transformer fault quality diagnosis using rough sets theory and neural network. It is rough sets reduction as the pre-unit of neural network based on reduction algorithm with the attribute significance. The paper describes the reduction algorithm and implementation method detailed. Through the training and testing results with practical data, it is proved that the reduction algorithm with the attribute significance can make the number of input samples shorter, the training speed faster and the diagnostic accuracy higher. The algorithm is feasible and effective for applying to the fault diagnosis system of power transformer.

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