A New Method of Neurofuzzy Network Based on Variable Precision Rough

2010 ◽  
Vol 40-41 ◽  
pp. 443-447
Author(s):  
Peng Hong ◽  
Wang Cong
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Linear Time ◽  
Decision Table ◽  
Optimal Method ◽  
Current Application ◽  
Variable Precision ◽  
Neuro Fuzzy ◽  
Variable Precision Rough Set ◽  
Fuzzy Network

In view of the current application deficiency of neuro-fuzzy network, a new optimal method of neurofuzzy network based on variable precision rough set is presented and its application in complex systems modeling is discussed. This method takes the β classification accuracy of variable precision rough set theory as information function to select the condition attribute, and then modeling data are discredited through selecting a proper precision to forms a decision table. Finally, the significant attributes and the key attribute values are extract from the decision table by using reduction algorithm based on variable precision, and are map pad into the fuzzy rules.It simplifies the fuzzy rules and therefore optimize the structure of neuro- fuzzy network effectively, reducing the training time of neural network greatly and improving the precision of training. This method has been applied to the modeling of non-linear time-delay system with a large number of sam-pling data, the validity and feasibility of this method is demonstrated by an example of modeling.

scholarly journals Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

2012 ◽  
Vol 9 (3) ◽  
pp. 1-17 ◽  
Author(s):  
D. Calvo-Dmgz ◽  
J. F. Gálvez ◽  
D. Glez-Peña ◽  
S. Gómez-Meire ◽  
F. Fdez-Riverola
Keyword(s):  
Gene Expression ◽  
Set Theory ◽  
Rough Set ◽  
Microarray Data ◽  
Rough Set Theory ◽  
Biological Knowledge ◽  
Variable Precision ◽  
Gene Sets ◽  
Proposed Model ◽  
Variable Precision Rough Set

Summary DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

scholarly journals A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set

2020 ◽  
Vol 38 (2) ◽  
pp. 434-441
Author(s):  
Dong Xu ◽  
Xin Wang ◽  
Yulong Meng ◽  
Ziying Zhang
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
Svm Classifier ◽  
Variable Precision ◽  
Optimal Subset ◽  
Potential Association ◽  
Discretization Algorithm ◽  
Single Attribute ◽  
Variable Precision Rough Set

Discretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attributes. Based on this, we proposed a discretization algorithm based on forest optimization and rough set (FORDA) in this paper. To solve the problem of discretization of multi-dimensional attributes, the algorithm designs the appropriate value function according to the variable precision rough set theory, and then constructs the forest optimization network and iteratively searches for the optimal subset of breakpoints. The experimental results on the UCI datasets show that:compared with the current mainstream discretization algorithms, the algorithm can avoid local optimization, significantly improve the classification accuracy of the SVM classifier, and its discretization performance is better, which verifies the effectiveness of the algorithm.

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.

Application of Variable Precision Rough Set in Power Transformer Fault Diagnosis

2013 ◽  
Vol 373-375 ◽  
pp. 824-828
Author(s):  
Shu Chuan Gan ◽  
Ai Hua Zhou ◽  
Hui Guo ◽  
Ling Tang
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Power Transformer ◽  
Fault Classification ◽  
Hidden Information ◽  
Variable Precision ◽  
Variable Precision Rough Set ◽  
Transformer Fault

The variable precision rough set theory is introduced into the fault diagnosis of power transformer. Using the reduction method of the variable precision rough set,the hidden information in power transformer faults data is reduced , and the information which plays a major role in fault classification can be obtained. This approach can overcome the defects of the classical rough set, such as the sensitivity to noise of input information, and accordingly improves the accuracy of fault diagnosis. The example shows that the variable precision rough set used in the power transformer fault diagnosis, enhance the robustness of the data analysis and processing, so, the proposed approach has a more effective diagnostic performance.

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.

Application of Variable Precision Rough Set and Integrated Neural Network to Bearing Fault Diagnosis

2013 ◽  
Vol 373-375 ◽  
pp. 1060-1063
Author(s):  
Xiao Ling Niu ◽  
Bo Liu ◽  
Ke Zhang Lin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimal Decision ◽  
Design System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Variable Precision ◽  
Variable Precision Rough Set

The integration of variable precision rough set and neural network is introduced into the bearing fault diagnosis. VPRS-INN fault diagnosis method is proposed: First, utilize the information entropy method for discretization of continuous attributes, and then use attribute dependence degree of the variable precision rough set theory for heuristic reduction. based on the reduction, obtain the optimal decision support system. Finally according to the optimal design system, we design a integrated neural network for fault diagnosis. instances have proved the feasibility and high fault diagnosis rate of the method.

Sign in / Sign up

Export Citation Format

Share Document