For real-world automatic sleep stage classification tasks, various existing deep learning based models are biased towards the majority with high proportion. Because of unique structure, most current polysomnography datasets suffer an inherent class imbalance problem (CIP), in which number each is severely unequal. In this study, we first define factor (CIF) to describe level CIP quantitatively....

Class imbalance problem are raised when one class having maximum number of examples than other classes. The classical classifiers of balance datasets cannot deal with the class imbalance problem because they pay more attention to the majority class. The main drawback associated with it majority class is loss of important information. The Class imbalance problem is a difficult due to the amount ...

Objective: Many of the machine learning classification algorithms are not robust against unbalanced classes and result in poorly accurate biased models. One way to address class imbalance is assign weights classes. This article proposes a new class-weighting approach improve problem when there an between two class. Material Methods: The performances formulation were compared with previously pro...

In a concept learning problem, imbalances in the distribution of the data can occur either between the two classes or within a single class. Yet, although both types of imbalances are known to affect negatively the performance of standard classifiers, methods for dealing with the class imbalance problem usually focus on rectifying the between-class imbalance problem, neglecting to address the i...