Communications of the ACM
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Learning cost-sensitive active classifiers
Artificial Intelligence
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Selective Sampling for Nearest Neighbor Classifiers
Machine Learning
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
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
Interruptible anytime algorithms for iterative improvement of decision trees
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Cost-Constrained Data Acquisition for Intelligent Data Preparation
IEEE Transactions on Knowledge and Data Engineering
Active learning for sampling in time-series experiments with application to gene expression analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Active sampling for detecting irrelevant features
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
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Anytime Learning of Decision Trees
The Journal of Machine Learning Research
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
Roulette Sampling for Cost-Sensitive Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
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
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
Any time induction of decision trees: an iterative improvement approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Thresholding for making classifiers cost-sensitive
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 unified approach to active dual supervision for labeling features and examples
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Fast data acquisition in cost-sensitive learning
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Active sampling for knowledge discovery from biomedical data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Selecting actions for resource-bounded information extraction using reinforcement learning
Proceedings of the fifth ACM international conference on Web search and data mining
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
Simple test strategies for cost-sensitive decision trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
Resource-Bounded information extraction: acquiring missing feature values on demand
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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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There is almost always a cost associated with acquiring training data. We consider the situation where the learner, with a fixed budget, may 'purchase' data during training. In particular, we examine the case where observing the value of a feature of a training example has an associated cost, and the total cost of all feature values acquired during training must remain less than this fixed budget. This paper compares methods for sequentially choosing which feature value to purchase next, given the budget and user's current knowledge of Naïve Bayes model parameters. Whereas active learning has traditionally focused on myopic (greedy) approaches and uniform/round-robin policies for query selection, this paper shows that such methods are often suboptimal and presents a tractable method for incorporating knowledge of the budget in the information acquisition process.