Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more timeconsuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling effort for single-label problems, current research on multi-label active learning remains in a preliminary state. In this paper, we first propose two novel multi-label active learning strategies, a max-margin prediction uncertainty strategy and a label cardinality inconsistency strategy, and then integrate them into an adaptive framework of multi-label active learning. Our empirical results on multiple multilabel data sets demonstrate the efficacy of the proposed active instance selection strategies and the integrated active learning approach.

23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
School of Computer Science

Li, X. (Xin), & Guo, Y. (2013). Active learning with multi-label SVM classification. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1479–1485).