用输入特征重要性解释训练小数据分类模型，提升准确性和可靠性。
Machine Learning from Explanations
- 通过交替优化预测准确率和注意力匹配解释信号来训练模型。
- 在小样本、类别不平衡或含虚假特征的数据上收敛更快更稳定。
- 适合数据稀缺或需可解释性的场景，如医疗、金融领域建模。
由于成本限制，大规模标注数据的获取与训练往往不切实际。小规模训练数据易导致模型结果波动大、过拟合及学习虚假关联。现有标签缺乏对标签分配理由的说明，使模型仅需符合标签即可，无法理解真实判别逻辑。为此，我们提出一种新方法：利用标注数据中简单的解释信号（如重要输入特征）训练小型数据集上的可靠分类模型。该方法采用两阶段训练循环，交替提升模型预测精度并优化其注意力以匹配解释信号，使模型在学习过程中理解标签背后的推理依据。实验表明，该训练循环能加速模型收敛至更准确、更可靠的解，尤其适用于小样本、类别不平衡或存在虚假特征的数据。
原文摘要 · Abstract (English)
Acquiring and training on large-scale labeled data can be impractical due to cost constraints. Additionally, the use of small training datasets can result in considerable variability in model outcomes, overfitting, and learning of spurious correlations. A crucial shortcoming of data labels is their lack of any reasoning behind a specific label assignment, causing models to learn any arbitrary classification rule as long as it aligns data with labels. To overcome these issues, we introduce an innovative approach for training reliable classification models on smaller datasets, by using simple explanation signals such as important input features from labeled data. Our method centers around a two-stage training cycle that alternates between enhancing model prediction accuracy and refining its attention to match the explanations. This instructs models to grasp the rationale behind label assignments during their learning phase. We demonstrate that our training cycle expedites the convergence towards more accurate and reliable models, particularly for small, class-imbalanced training data, or data with spurious features.
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