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
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Representative sampling for text classification using support vector machines
ECIR'03 Proceedings of the 25th European conference on IR research
Active learning with history-based query selection for text categorisation
ECIR'07 Proceedings of the 29th European conference on IR research
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
MP-Boost: a multiple-pivot boosting algorithm and its application to text categorization
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
A weakly-supervised approach to argumentative zoning of scientific documents
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Active learning for hierarchical text classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A utility-theoretic ranking method for semi-automated text classification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Active learning with multi-label SVM classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabeled examples in terms of how much further information they would carry, once manually labeled, for retraining a (hopefully) better classifier. Research on active learning in text classification has so far concentrated on single-label classification; active learning for multi-label classification, instead, has either been tackled in a simulated (and, we contend, non-realistic) way, or neglected tout court . In this paper we aim to fill this gap by examining a number of realistic strategies for tackling active learning for multi-label classification. Each such strategy consists of a rule for combining the outputs returned by the individual binary classifiers as a result of classifying a given unlabeled document. We present the results of extensive experiments in which we test these strategies on two standard text classification datasets.