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Multi-label imbalanced data comprise data with a disproportionate number of samples in the classes. Traditional classifiers are more suitable for classifying balanced data because the classification performance declines dramatically when the class sizes are imbalanced in multi-label data. In this study, we propose an algorithm that assesses the cost of the majority class and the value of the minority...
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