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- W4220772675 abstract "Several oversampling methods have been proposed for solving the class imbalance problem. However, most of them require searching the k-nearest neighbors to generate synthetic objects. This requirement makes them time-consuming and therefore unsuitable for large datasets. In this paper, an oversampling method for large class imbalance problems that do not require the k-nearest neighbors’ search is proposed. According to our experiments on large datasets with different sizes of imbalance, the proposed method is at least twice as fast as 8 the fastest method reported in the literature while obtaining similar oversampling quality." @default.
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- W4220772675 date "2022-03-28" @default.
- W4220772675 modified "2023-10-16" @default.
- W4220772675 title "An Oversampling Method for Class Imbalance Problems on Large Datasets" @default.
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- W4220772675 doi "https://doi.org/10.3390/app12073424" @default.
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