Multi-label Active Learning with Auxiliary Learner


Chen-Wei Hung, Hsuan-Tien Lin ;
Proceedings of the Asian Conference on Machine Learning, PMLR 20:315-332, 2011.


Multi-label active learning is an important problem because of the expensive labeling cost in multi-label classification applications. A state-of-the-art approach for multi-label active learning, maximum loss reduction with maximum confidence (MMC), heavily depends on the binary relevance support vector machine in both learning and querying. Nevertheless, it is not clear whether the heavy dependence is necessary or unrivaled. In this work, we extend MMC to a more general framework that removes the heavy dependence and clarifies the roles of each component in MMC. In particular, the framework is characterized by a major learner for making predictions, an auxiliary learner for helping with query decisions and a query criterion based on the disagreement between the two learners. The framework takes MMC and several baseline multi-label active learning algorithms as special cases. With the flexibility of the general framework, we design two criteria other than the one used by MMC. We also explore the possibility of using learners other than the binary relevance support vector machine for multi-label active learning. Experimental results demonstrate that a new criterion, soft Hamming loss reduction, is usually better than the original MMC criterion across different pairs of major/auxiliary learners, and validate the usefulness of the proposed framework.

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