Active learning with multi-label SVM classification
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.
|Conference||23rd International Joint Conference on Artificial Intelligence, IJCAI 2013|
Li, X. (Xin), & Guo, Y. (2013). Active learning with multi-label SVM classification. In IJCAI International Joint Conference on Artificial Intelligence (pp. 1479–1485).