Random analysis of statistical rough set

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical roug...

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Bibliographic Details
Published in2016 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 1; pp. 43 - 47
Main Authors Tsang, Eric C. C., Su-Yun Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2016
Subjects
Absolutely Error
Algorithm design and analysis
Attribute Reduction
Big data
Cybernetics
Fluctuations
Randomly Sampling
Rough sets
Sample size
Stability analysis
The quantile
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Summary:Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling.
ISSN:2160-1348
DOI:10.1109/ICMLC.2016.7860875