Abstract
Datasets with imbalanced class distribution are available in various real-world applications. A great number of approaches has been proposed to address the class imbalance challenge, but most of these models perform poorly when datasets are characterized with high class imbalance, class overlap and low data quality. In this study, we propose an effective meta-framework for high imbalance overlapped classification, called DAPS (DynAmic self-Paced sampling enSemble), which (1) leverages reasonable and effective sampling to maximize the utilization of informative instances and to avoid serious information loss and (2) assigns proper instance weights to address the issues of noisy data. Furthermore, most of the existing canonical classifiers (e.g. Decision Tree, Random Forest) can be integrated in DAPS. The comprehensive experimental results on both synthetic and three real-world datasets show that the DAPS model could obtain considerable improvements in F1-score when compared to a broad range of published models.
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The code is available at https://github.com/ZhouF-ECNU/DAPS.
Due to space limitation, we only report precision and recall results on real-world datasets. AUPRC (i.e., the area under the precision-recall curve) does not properly reflect the performance of our model, as DAPS chooses 0.5 as threshold to optimize predictions.
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This research was supported in part by NSFC Grant 61902127 and Natural Science Foundation of Shanghai 19ZR1415700.
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Zhou, F., Gao, S., Ni, L. et al. Dynamic self-paced sampling ensemble for highly imbalanced and class-overlapped data classification. Data Min Knowl Disc 36, 1601–1622 (2022). https://doi.org/10.1007/s10618-022-00838-z
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DOI: https://doi.org/10.1007/s10618-022-00838-z