基于图拍卖动态的多分类主动学习框架，提升标注效率。
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
- 用图上拍卖算法优化标签选择，兼顾相似样本与分类边界。
- 新提出基于对偶变量的不确定性度量，精准定位难分样本。
- 在多个数据集上优于现有方法，适合少样本分类场景。
主动学习通过有策略地选择少量未标记样本进行标注，提升机器学习模型性能，尤其在半监督场景中。本文提出多分类主动学习框架 MALADY，利用图上的拍卖动态算法实现高效主动学习。我们拓展了文献[24]中针对相似性图的拍卖算法，引入更通用的优化目标函数；同时设计了一种新的主动学习采集函数，利用拍卖算法的对偶变量衡量分类器不确定性，优先选择位于不同类别决策边界附近的样本进行标注。在多个分类任务上的实验表明，该方法性能优于对比算法。
原文摘要 · Abstract (English)
Active learning enhances the performance of machine learning methods, particularly in semi-supervised cases, by judiciously selecting a limited number of unlabeled data points for labeling, with the goal of improving the performance of an underlying classifier. In this work, we introduce the Multiclass Active Learning with Auction Dynamics on Graphs (MALADY) framework which leverages the auction dynamics algorithm on similarity graphs for efficient active learning. In particular, we generalize the auction dynamics algorithm on similarity graphs for semi-supervised learning in [24] to incorporate a more general optimization functional. Moreover, we introduce a novel active learning acquisition function that uses the dual variable of the auction algorithm to measure the uncertainty in the classifier to prioritize queries near the decision boundaries between different classes. Lastly, using experiments on classification tasks, we evaluate the performance of our proposed method and show that it exceeds that of comparison algorithms.
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