COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Machine Learning
Machine Learning
Learning cost-sensitive active classifiers
Artificial Intelligence
Machine Learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Active Sampling for Class Probability Estimation and Ranking
Machine Learning
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Active cost-sensitive learning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
Curiosity driven incremental LDA agent active learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Asking generalized queries to ambiguous oracle
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an active learner may request labels of only selected instances, thus reducing labeling work dramatically. Most previous works of active learning are, however, pool-based; that is, a pool of unlabeled examples is given and the learner can only select examples from the pool to query for their labels. This type of active learning has several weaknesses. In this paper we propose novel active learning algorithms that construct examples directly to query for labels. We study both a specific active learner based on the decision tree algorithm, and a general active learner that can work with any base learning algorithm. As there is no restriction on what examples to be queried, our methods are shown to often query fewer examples to reduce the predictive error quickly. This casts doubt on the usefulness of the pool in pool-based active learning. Nevertheless, our methods can be easily adapted to work with a given pool of unlabeled examples.