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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Decision-Centric Active Learning of Binary-Outcome Models
Information Systems Research
Active Feature-Value Acquisition
Management Science
Selective supervision: guiding supervised learning with decision-theoretic active learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Optimizing debt collections using constrained reinforcement learning
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Machine learning for science and society
Machine Learning
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Data-driven predictive models are routinely used by government agencies and industry to improve the efficiency of their decision-making. In many cases, agencies acquire training data over time, incurring both direct and opportunity costs. Active learning can be used to acquire particularly informative training data that improve learning cost-effectively. However, when multiple models are used to inform decisions, prior work on active learning has significant limitations: either it improves the accuracy of predictive models without regard to how accuracy affects decision making or it addresses only decisions informed by a single predictive model. We propose that decisions informed by multiple models warrant a new kind of Collaborative Information Acquisition (CIA) policy that allows multiple learners to reason collaboratively about informative acquisitions. This paper focuses on tax audit decisions, which affect a vital revenue source for governments worldwide. Because audits are costly to conduct, active learning policies can help identify particularly informative audits to improve future decisions. However, existing active learning models are poorly suited to audit decisions, because audits are best informed by multiple predictive models. We develop a CIA policy to improve the decisions the models inform, and we demonstrate that CIA can substantially increase sales tax revenues. We also demonstrate that the CIA policy can improve decisions to target directly individuals in a donation campaign. Finally, we discuss and demonstrate the risks for decision making of the naive use of existing active learning policies.