MALADY：基于图上拍卖动力学的多类主动学习
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
AI总结：
针对主动学习在半监督场景的优化需求，提出MALADY框架，泛化相似图拍卖动力学算法并引入基于对偶变量的获取函数，实验表明其性能优于对比算法。
摘要：
主动学习通过明智地选择有限数量的未标记数据点进行标记，以提高底层分类器的性能，尤其在半监督情况下能增强机器学习方法的性能。在这项工作中，我们提出了基于图上拍卖动力学的多类主动学习（MALADY）框架，该框架利用相似图上的拍卖动力学算法实现高效主动学习。具体而言，我们将文献[24]中用于半监督学习的相似图拍卖动力学算法进行泛化，以纳入更通用的优化泛函。此外，我们引入了一种新颖的主动学习获取函数，该函数利用拍卖算法的对偶变量来衡量分类器的不确定性，从而优先查询不同类别决策边界附近的数据点。最后，通过分类任务的实验，我们评估了所提方法的性能，并表明其优于对比算法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
