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Learning from data sets that contain very few instances of the minority class usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over the minority class. SMOTE (synthetic minority over-sampling technique) is specifically designed for learning from imbalanced data sets. This paper presents a modified approach (MSMOTE) for...
The key theorem plays an important role in the statistical learning theory. However, the researches about it at present mainly focus on real random variable and the samples which are supposed to be noise-free. In this paper, the definitions of complex rough variable and primary norm are introduced. Then, the definitions of the complex empirical risk functional, the complex expected risk functional,...
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