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• W4377229802 endingPage "110665" @default.
• W4377229802 startingPage "110665" @default.
• W4377229802 abstract "Learning class-imbalance data has become a challenging task in machine learning. Oversampling is an effective way to achieve rebalancing between classes by generating new minority instances. However, most existing oversampling methods exhibit significant ill-posedness due to the involvement of k-nearest neighbors. Recently, several novel techniques have been developed using natural neighbors instead of k-nearest neighbors. But these budding techniques fail to take into account the natural distribution of data, resulting in degraded adaptability of natural neighbors to data characteristics. In this paper, we propose an adaptive and completely parameter-free synthetic oversampling method called NanBDOS (short for borderline oversampling via natural neighbor search). NanBDOS first determines the intrinsic neighbor relationship between instances through a natural neighbor search procedure. Then, those informative borderline instances are identified and used as base instances, i.e., sampling seeds. NanBDOS assigns dynamic sampling weights to the base instances so that the data complexity can be well represented. Finally, new synthetic instances are generated by interpolating between the base instances and their natural neighbors. NanBDOS is experimentally compared with seven baseline methods on twenty-four real-world datasets. The results confirm the effectiveness of the proposed method. Moreover, the statistical analysis also indicates its higher-level Friedman ranking." @default.
• W4377229802 created "2023-05-23" @default.
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• W4377229802 date "2023-08-01" @default.
• W4377229802 modified "2023-10-15" @default.
• W4377229802 title "NanBDOS: Adaptive and parameter-free borderline oversampling via natural neighbor search for class-imbalance learning" @default.
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• W4377229802 doi "https://doi.org/10.1016/j.knosys.2023.110665" @default.
• W4377229802 hasPublicationYear "2023" @default.
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