Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Reinforcement learning for active model selection
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
Feature value acquisition in testing: a sequential batch test algorithm
ICML '06 Proceedings of the 23rd international conference on Machine learning
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Partial example acquisition in cost-sensitive learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce
Proceedings of the ninth international conference on Electronic commerce
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
Customer targeting models using actively-selected web content
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases
ACM Transactions on Knowledge Discovery from Data (TKDD)
Active Feature-Value Acquisition
Management Science
Cost-sensitive test strategies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
A probabilistic search for the best solution among partially completed candidates
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Induction over Strategic Agents
Information Systems Research
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Using classifier cascades for scalable e-mail classification
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Fast data acquisition in cost-sensitive learning
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
The Journal of Machine Learning Research
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
New algorithms for budgeted learning
Machine Learning
Intelligently querying incomplete instances for improving classification performance
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
Collaborative information acquisition for data-driven decisions
Machine Learning
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Many induction problems include missing data that can be acquired at a cost. For building accurate predictive models, acquiring complete information for all instances is often expensive or unnecessary, while acquiring information for a random subset of instances may not be most effective. Active feature-value acquisition tries to reduce the cost of achieving a desired model accuracy by identifying instances for which obtaining complete information is most informative. We present an approach in which instances are selected for acquisition based on the current model's accuracy and its confidence in the prediction. Experimental results demonstrate that our approach can induce accurate models using substantially fewer feature-value acquisitions as compared to alternative policies.