MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
This work addresses the challenge of efficient data labeling in semi-supervised learning for multiclass classification, representing an incremental improvement over existing methods.
The authors tackled the problem of improving active learning for multiclass classification by introducing the MALADY framework, which uses auction dynamics on graphs and a novel acquisition function based on dual variables to prioritize queries near decision boundaries, and they showed it outperforms comparison algorithms in experiments.
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.