用数据内在维度衡量类别不平衡，无需训练模型即可提升效果。
Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance
- 以数据内在维度代替样本数量，无须训练模型即可评估不平衡程度。
- 在五个数据集上，该方法显著优于传统重加权和重采样策略。
- 结合样本数与内在维度能进一步提升分类性能，适合不平衡数据场景。
分类任务中的不平衡通常通过各类别样本数量来衡量，但这种方法忽略了冗余样本的存在以及类别间学习难度的差异。现有复杂度量如训练损失和不确定性则依赖于模型训练。本文提出使用数据内在维度（ID）作为一种简单易算、无需模型的不平衡度量方式，可无缝集成到多种不平衡缓解方法中。在五个具有不同不平衡比率的数据集上的实验表明，ID始终优于基于样本数的重加权和重采样技术。此外，将ID与样本数量结合使用可进一步提升性能。代码与模型已开源：https://github.com/cagries/IDIM。
原文摘要 · Abstract (English)
Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent differences in the learning difficulties of classes. Alternatively, one can use complex measures such as training loss and uncertainty, which, however, depend on training a machine learning model. Our paper proposes using data Intrinsic Dimensionality (ID) as an easy-to-compute, model-free measure of imbalance that can be seamlessly incorporated into various imbalance mitigation methods. Our results across five different datasets with a diverse range of imbalance ratios show that ID consistently outperforms cardinality-based re-weighting and re-sampling techniques used in the literature. Moreover, we show that combining ID with cardinality can further improve performance. Our code and models are available at https://github.com/cagries/IDIM.
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