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It acquaints the class imbalance problem.
Three objectives are devised considering the class imbalance context.
The problem is well known as the class imbalance.
Class prior re-weighting is widely applied in the class imbalance problem, while, in multi-label learning scenario, researchers found that the class imbalance problem is even serious and the class imbalance ratio may also change overtime [23].
Classification approaches usually present the poor generalization performance with an apparent class imbalance problem.
The modelling techniques are based on Support Vector Machines (SVMs) which are sensitive to class imbalance.
To handle class imbalance, the training data are resampled in two stages.
Class imbalance and labeling errors present significant challenges to data mining and knowledge discovery applications.
These are standard datasets used in literature which have varying levels of binary class imbalance.
The result is a high class imbalance in most training and evaluation datasets.
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All the fourteen datasets showed class imbalance--there were more samples in case group than in control group.
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