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A tutorial on learning with Bayesian networks
Learning in graphical models
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning cost-sensitive active classifiers
Artificial Intelligence
Artificial Intelligence: A Modern Approach
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Active Feature-Value Acquisition for Classifier Induction
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Reinforcement learning for active model selection
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
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Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Reinforcement learning for active model selection
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Maximizing classifier utility when training data is costly
ACM SIGKDD Explorations Newsletter
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
Active learning with direct query construction
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and 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
Maximizing classifier utility when there are data acquisition and modeling costs
Data Mining and Knowledge Discovery
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases
ACM Transactions on Knowledge Discovery from Data (TKDD)
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Resource-bounded information gathering for correlation clustering
COLT'07 Proceedings of the 20th annual conference on Learning theory
A decision support system for cost-effective diagnosis
Artificial Intelligence in Medicine
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Interactive learning for efficiently detecting errors in insurance claims
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast data acquisition in cost-sensitive learning
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
ACE-Cost: acquisition cost efficient classifier by hybrid decision tree with local SVM leaves
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Efficient Learning with Partially Observed Attributes
The Journal of Machine Learning Research
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
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Machine Learning
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Repeated labeling using multiple noisy labelers
Data Mining and Knowledge Discovery
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Since resources for data acquisition are seldom infinite, both learners and classifiers must act intelligently under hard budgets. In this paper, we consider problems in which feature values are unknown to both the learner and classifier, but can be acquired at a cost. Our goal is a learner that spends its fixed learning budget bL acquiring training data, to produce the most accurate “active classifier” that spends at most bC per instance. To produce this fixed-budget classifier, the fixed-budget learner must sequentially decide which feature values to collect to learn the relevant information about the distribution. We explore several approaches the learner can take, including the standard “round robin” policy (purchasing every feature of every instance until the bL budget is exhausted). We demonstrate empirically that round robin is problematic (especially for small bL), and provide alternate learning strategies that achieve superior performance on a variety of datasets.