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Nearest neighbors and density-based undersampling for imbalanced data classification with class overlap
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DOI：10.1016/j.neucom.2024.128492.png)
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While addressing the problem of imbalanced data classification, most existing resampling methods primarily focus on balancing class distribution. However, they often overlook class overlap and fail to adequately consider the feature distributions of different classes. Consequently, when resampling is performed under conditions, samples within areas of overlap remain susceptible to misclassification, failing to substantially improve overall performance. To address these shortcomings, we propose a novel data resampling technique, Nearest Neighbors and Density-based Undersampling (NDU). This method employs within-class k neighbors and between-class probability densities to design a weight assignment strategy. Leveraging strategy, we establish an exclusive metric, the F_factor, to evaluate the importance of majority class samples overlap areas. Subsequently, NDU promotes a gradient-based segmented undersampling strategy, which varying degrees of undersampling to majority class samples across segmented regions. Through experiments on binary imbalanced datasets with class overlap, we evaluate the efficiency of diverse resampling concerning classification performance. The results demonstrate that our proposed method effectively addresses class overlap challenges.
Keyword:
Imbalanced classification
Class overlap
K-nearest neighbor
Probability density
Undersampling
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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