Instance-Based Learning Algorithms
Machine Learning
The weighted majority algorithm
Information and Computation
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
Some label efficient learning results
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Interactive machine learning: letting users build classifiers
International Journal of Human-Computer Studies
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Argument based machine learning
Artificial Intelligence
Interacting meaningfully with machine learning systems: Three experiments
International Journal of Human-Computer Studies
Bayesian network models for hierarchical text classification from a thesaurus
International Journal of Approximate Reasoning
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Simulating user intervention for interactive semantic place recognition with mobile devices
Proceedings of the 2012 RecSys workshop on Personalizing the local mobile experience
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We consider the scenario in which an automatic classifier (previously built) is available. It is used to classify new instances but, in some cases, the classifier may request the intervention of a human (the oracle), who gives it the correct class. In this scenario, first it is necessary to study how the performance of the system should be evaluated, as it cannot be based solely on the predictive accuracy obtained by the classifier but it should also take into account the cost of the human intervention; second, studying the concrete circumstances under which the classifier decides to query the oracle is also important. In this paper we study these two questions and include also an experimental evaluation of the different proposed alternatives.