用相似度找未标注数据，自动补足少数标签文本。
A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data
- 基于实例相似度，在未标注数据中寻找与少数类相近的样本。
- 迭代筛选能提升分类器性能的样本，加入训练集。
- 适合标签不平衡的多标签文本分类任务，无需人工标注。
在真实应用场景中，尽管数据量不断增加，但机器学习项目仍面临标注成本高、耗时长的问题。许多多标签分类任务还存在数据不平衡现象，某些类别缺乏足够训练样本。本文提出一种新型多标签文本数据过采样方法，通过计算实例间的相似度，从未标注数据中识别潜在的新样本。该方法迭代搜索未标注数据集，定位与少数类相似的实例，并评估其对分类器性能的提升效果。表现优异的实例将被加入已有标注数据集。实验表明，该方法能有效提升分类性能，缓解数据不平衡带来的挑战。
原文摘要 · Abstract (English)
In real-world applications, as data availability increases, obtaining labeled data for machine learning (ML) projects remains challenging due to the high costs and intensive efforts required for data annotation. Many ML projects, particularly those focused on multi-label classification, also grapple with data imbalance issues, where certain classes may lack sufficient data to train effective classifiers. This study introduces and examines a novel oversampling method for multi-label text classification, designed to address performance challenges associated with data imbalance. The proposed method identifies potential new samples from unlabeled data by leveraging similarity measures between instances. By iteratively searching the unlabeled dataset, the method locates instances similar to those in underrepresented classes and evaluates their contribution to classifier performance enhancement. Instances that demonstrate performance improvement are then added to the labeled dataset. Experimental results indicate that the proposed approach effectively enhances classifier performance post-oversampling.
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