Learning from combination of data chunks for multi-class imbalanced data


Class-imbalance is very common in real-world applications. Previous studies focused on binary-class imbalance problem, whereas multi-class imbalance problem is more general and more challenging. Under-sampling is an effective and efficient method for binary-class imbalanced data. But when it is used for multi-class imbalanced data, many more majority class… (More)
DOI: 10.1109/IJCNN.2014.6889667


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