Variable Precision Rough Set Model in Information Tables with Missing Values

Author(s):  
Yoshifumi Kusunoki ◽  
◽  
Masahiro Inuiguchi

In this paper, we study rough set models in information tables with missing values. The variable precision rough set model proposed by Ziarko tolerates misclassification error using a membership function in complete information tables. We generalize the variable precision rough set in information tables with missing values. Because of incompleteness, the membership degree of each objects becomes an interval value. We define six different approximate regions using the lower and upper bounds of membership functions. The properties of the proposed rough set model are investigated. Moreover we show that the proposed model is a generalization of rough set models based on similarity relations.

2013 ◽  
Vol 694-697 ◽  
pp. 2856-2859
Author(s):  
Mei Yun Wang ◽  
Chao Wang ◽  
Da Zeng Tian

The variable precision probabilistic rough set model is based on equivalent relation and probabilistic measure. However, the requirements of equivalent relation and probabilistic measure are too strict to satisfy in some practical applications. In order to solve the above problem, a variable precision rough set model based on covering relation and uncertainty measure is proposed. Moreover, the upper and lower approximation operators of the proposed model are given, while the properties of the operators are discussed.


2012 ◽  
Vol 9 (3) ◽  
pp. 1-17 ◽  
Author(s):  
D. Calvo-Dmgz ◽  
J. F. Gálvez ◽  
D. Glez-Peña ◽  
S. Gómez-Meire ◽  
F. Fdez-Riverola

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.


2014 ◽  
Vol 599-601 ◽  
pp. 1350-1356
Author(s):  
Ming Ming Jia ◽  
Hai Qin Qin ◽  
Yong Qi Wang ◽  
Ke Jun Xu

A new neighborhood variable precision rough set modal is presented in this paper. The modal possesses the characteristics of neighborhood rough set and variable precision rough set, so it can overcome shortcomings of classic rough set which only be fit for discrete variables and sensitive to noise. Based on giving the definitions of approximate reduction, lower and upper approximate reduction, lower and upper distribution reduction, two kinds of algorithms to confirm lower and upper distribution reduction were advanced. The modal was applied to diagnose one frequency modulated water pump vibration faults. The result shows the modal is more suitable to engineering problems, because it can not only deal with continues variables but also be robust to noise.


2014 ◽  
Vol 631-632 ◽  
pp. 49-52
Author(s):  
Yan Li ◽  
Jia Jia Hou ◽  
Xiao Qing Liu

Variable precision rough set (VPRS) based on dominance relation is an extension of traditional rough set by which can handle preference-ordered information flexibly. This paper focuses on the maintenance of approximations in dominance based VPRS when the objects in an information system vary over time. The incremental updating principles are given as inserting or deleting an object, and some experimental evaluations validates the effectiveness of the proposed method.


Author(s):  
Wei Zhang

To enable the teaching administrator to better obtain effective knowledge from a large amount of information to assist management and improve the efficiency and level of teaching management, a variable precision rough set model for knowledge assisted management of distance education was proposed. First, based on the theory of complete reduction and knowledge extraction, the proposed pedigree ambiguity tree was used as a strategy for obtaining complete reduction. An algorithm for obtaining a complete set of reductions was given. Then, by studying the process of knowledge extraction, a multi-knowledge extraction framework was put forward. The process of data conversion was completely realized. Finally, experimental verification was performed. The results showed that the proposed model overcame the effect of noise data in real data and improved the efficiency of the algorithm. Therefore, the model has high universality.


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