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- W4287183744 abstract "Real-world classification domains, such as medicine, health and safety, and finance, often exhibit imbalanced class priors and have asynchronous misclassification costs. In such cases, the classification model must achieve a high recall without significantly impacting precision. Resampling the training data is the standard approach to improving classification performance on imbalanced binary data. However, the state-of-the-art methods ignore the local joint distribution of the data or correct it as a post-processing step. This can causes sub-optimal shifts in the training distribution, particularly when the target data distribution is complex. In this paper, we propose Radial-Based Combined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class potential to refine the energy-based resampling approach of CCR. In particular, RB-CCR exploits the class potential to accurately locate sub-regions of the data-space for synthetic oversampling. The category sub-region for oversampling can be specified as an input parameter to meet domain-specific needs or be automatically selected via cross-validation. Our $5times2$ cross-validated results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR achieves a better precision-recall trade-off than CCR and generally out-performs the state-of-the-art resampling methods in terms of AUC and G-mean." @default.
- W4287183744 created "2022-07-25" @default.
- W4287183744 creator A5027393953 @default.
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- W4287183744 date "2021-05-09" @default.
- W4287183744 modified "2023-10-18" @default.
- W4287183744 title "RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification" @default.
- W4287183744 doi "https://doi.org/10.48550/arxiv.2105.04009" @default.
- W4287183744 hasPublicationYear "2021" @default.
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