A Supervised and Multivariate Discretization Algorithm for Rough Sets

2010 ◽  
pp. 596-603 ◽  
Author(s):  
Feng Jiang ◽  
Zhixi Zhao ◽  
Yan Ge
Keyword(s):  
Rough Sets ◽  
Discretization Algorithm ◽  
Multivariate Discretization

scholarly journals Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

2008 ◽  
Vol 2008 ◽  
pp. 1-13 ◽  
Author(s):  
Aboul ella Hassanien ◽  
Mohamed E. Abdelhafez ◽  
Hala S. Own
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Confusion Matrix ◽  
Decision Rules ◽  
Minimal Subset ◽  
Discretization Algorithm ◽  
Diabetic Children ◽  
The Relationship ◽  
Accuracy Rates

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

scholarly journals Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1245
Author(s):  
Xiangyang Li ◽  
Yangyang Shen
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Rough Set ◽  
Rough Sets ◽  
Application Performance ◽  
Economic Information ◽  
Related Information ◽  
Discretization Algorithm ◽  
Continuous Attribute ◽  
Relationship Of

Discretization based on rough sets is used to divide the space formed by continuous attribute values with as few breakpoint sets as possible, while maintaining the original indistinguishable relationship of the decision system, so as to accurately classify and identify related information. In this study, a discretization algorithm for incomplete economic information in rough set based on big data is proposed. First, the algorithm for filling-in incomplete economic information based on deep learning is used to supplement the incomplete economic information. Then, based on breakpoint discrimination, the algorithm for discretization in the rough set is used to implement the discretization based on rough set for supplementary economic information. The performance of this algorithm was tested using multiple sets of data and compared with other algorithms. Experimental results show that this algorithm is effective for discretization based on a rough set of incomplete economic information. When the number of incomplete economic information rough candidate breakpoints increases, it still has a higher computational efficiency and can effectively improve the integrity of incomplete economic information, and finally the application performance is superior.

An ICA-Based Multivariate Discretization Algorithm

2006 ◽  
pp. 556-562 ◽  
Author(s):  
Ye Kang ◽  
Shanshan Wang ◽  
Xiaoyan Liu ◽  
Hokyin Lai ◽  
Huaiqing Wang ◽  
...  
Keyword(s):  
Discretization Algorithm ◽  
Multivariate Discretization

BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES

1999 ◽  
Vol 04 (01) ◽  
Author(s):  
C. Zopounidis ◽  
M. Doumpos ◽  
R. Slowinski ◽  
R. Susmaga ◽  
A. I. Dimitras
Keyword(s):  
Multivariate Analysis ◽  
Rough Sets ◽  
Failure Prediction ◽  
Business Failure ◽  
Analysis Techniques

Approximation Sets of Rough Sets

2012 ◽  
Vol 23 (7) ◽  
pp. 1745-1759 ◽  
Author(s):  
Qing-Hua ZHANG ◽  
Guo-Yin WANG ◽  
Yu XIAO
Keyword(s):  
Rough Sets

Analysing diabetes using rough sets

2020 ◽  
pp. 533-538
Author(s):  
S. Arjun Raj ◽  
M. Vigneshwaran
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
International Diabetes Federation ◽  
Published Data ◽  
Lower And Upper Approximations

In this article we use the rough set theory to generate the set of decision concepts in order to solve a medical problem.Based on officially published data by International Diabetes Federation (IDF), rough sets have been used to diagnose Diabetes.The lower and upper approximations of decision concepts and their boundary regions have been formulated here.

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