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- W4244222110 abstract "The imbalanced learning methods aim to learn the unbiased classification models from massive class-imbalanced datasets. However, due to the data distributions affected by noise and borderline samples, many conventional resampling methods based on heuristic assumptions suffer from the lack of practicability and accuracy for real-world classification tasks. In order to effectively cope with these issues, in this work, we propose a novel Stratified Radial Based Oversampling (SRBO) oversampling method for imbalanced massive data classification. Starting from the data level, we first divide minority samples into safe points, dangerous points, and noise points, and the number of new points is allocated according to the number of the three types of samples, and then over-sampling is performed based on the Radial Based Oversampling (RBO). Compared with two state-of-the-art resampling classification methods 10 public imbalanced datasets, our method yields comparable or better results on the effectiveness and accuracy." @default.
- W4244222110 created "2022-05-12" @default.
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- W4244222110 date "2021-10-01" @default.
- W4244222110 modified "2023-09-27" @default.
- W4244222110 title "Stratified Radial Based Oversampling for Imbalanced Data Classification" @default.
- W4244222110 doi "https://doi.org/10.1109/ainit54228.2021.00133" @default.
- W4244222110 hasPublicationYear "2021" @default.
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