COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active learning with multiple views
Active learning with multiple views
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Active learning for probability estimation using jensen-shannon divergence
ECML'05 Proceedings of the 16th European conference on Machine Learning
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Confidence-based stopping criteria for active learning for data annotation
ACM Transactions on Speech and Language Processing (TSLP)
Cost-minimising strategies for data labelling: optimal stopping and active learning
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
Active learning for protein function prediction in protein-protein interaction networks
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Autonomously learning to visually detect where manipulation will succeed
Autonomous Robots
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Ensemble-based active learning has been proven to efficiently reduce the number of training instances and thus the cost of data acquisition. To determine the utility of a candidate training instance, the disagreement about its class value among the ensemble members is used. While the disagreement for binary classification is easily determined using margins, the adaption to multi-class problems is not straightforward and little studied in the literature. In this paper we consider four approaches to measure ensemble disagreement, including margins, uncertainty sampling and entropy, and evaluate them empirically on various ensemble strategies for active learning. We show that margins outperform the other disagreement measures on three of four active learning strategies. Our experiments also show that some active learning strategies are more sensitive to the choice of disagreement measure than others.