COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Information-based objective functions for active data selection
Neural Computation
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On Active Learning for Data Acquisition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Active learning with statistical models
Journal of Artificial Intelligence Research
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Active sampling for knowledge discovery from biomedical data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Active Feature-Value Acquisition
Management Science
Active learning for directed exploration of complex systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active Learning of Instance-Level Constraints for Semi-supervised Document Clustering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
New algorithms for budgeted learning
Machine Learning
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The general approach for automatically driving data collection using information from previously acquired data is called active learning. Traditional active learning addresses the problem of choosing the unlabeled examples for which the class labels are queried with the goal of learning a classifier. In contrast we address the problem of active feature sampling for detecting useless features. We propose a strategy to actively sample the values of new features on class-labeled examples, with the objective of feature relevance assessment. We derive an active feature sampling algorithm from an information theoretic and statistical formulation of the problem. We present experimental results on synthetic, UCI and real world datasets to demonstrate that our active sampling algorithm can provide accurate estimates of feature relevance with lower data acquisition costs than random sampling and other previously proposed sampling algorithms.