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Improving Generalization with Active Learning
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Selective Sampling Using the Query by Committee Algorithm
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Selective sampling for nearest neighbor classifiers
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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Learning cost-sensitive active classifiers
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IEEE Internet Computing
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Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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IBM Systems Journal
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Toward economic machine learning and utility-based data mining
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
An Expected Utility Approach to Active Feature-Value Acquisition
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
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Proceedings of the 15th international conference on World Wide Web
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ICML '06 Proceedings of the 23rd international conference on Machine learning
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Decision-Centric Active Learning of Binary-Outcome Models
Information Systems Research
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AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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ECML'05 Proceedings of the 16th European conference on Machine Learning
Active learning for probability estimation using jensen-shannon divergence
ECML'05 Proceedings of the 16th European conference on Machine Learning
Effective label acquisition for collective classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Audience selection for on-line brand advertising: privacy-friendly social network targeting
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Active dual supervision: reducing the cost of annotating examples and features
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
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ACM Transactions on Knowledge Discovery from Data (TKDD)
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Electronic commerce is revolutionizing the way we think about data modeling, by making it possible to integrate the processes of (costly) data acquisition and model induction. The opportunity for improving modeling through costly data acquisition presents itself for a diverse set of electronic commerce modeling tasks, from personalization to customer lifetime value modeling; we illustrate with the running example of choosing offers to display to web-site visitors, which captures important aspects in a familiar setting. Considering data acquisition costs explicitly can allow the building of predictive models at significantly lower costs, and a modeler may be able to improve performance via new sources of information that previously were too expensive to consider. However, existing techniques for integrating modeling and data acquisition cannot deal with the rich environment that electronic commerce presents. We discuss several possible data acquisition settings, the challenges involved in the integration with modeling, and various research areas that may supply parts of an ultimate solution. We also present and demonstrate briefly a unified framework within which one can integrate acquisitions of different types, with any cost structure and any predictive modeling objective.