A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A family of additive online algorithms for category ranking
The Journal of Machine Learning Research
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Active Learning to Recognize Multiple Types of Plankton
The Journal of Machine Learning Research
ECML '07 Proceedings of the 18th European conference on Machine Learning
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Active Learning Strategies for Multi-Label Text Classification
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Effective multi-label active learning for text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Reducing labeling effort for structured prediction tasks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Optimistic active learning using mutual information
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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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 time-consuming 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.