2022
DOI: 10.48550/arxiv.2201.03957
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Multi-granularity Relabeled Under-sampling Algorithm for Imbalanced Data

Abstract: The imbalanced classification problem turns out to be one of the important and challenging problems in data mining and machine learning. The performances of traditional classifiers will be severely affected by many data problems, such as class imbalanced problem, class overlap and noise. When the number of one class in the data set is larger than other classes, class imbalanced problem will inevitably occur. Therefore, many researchers are committed to solving the problem of category imbalance and improving th… Show more

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