A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Learning cost-sensitive active classifiers
Artificial Intelligence
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On Active Learning for Data Acquisition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Diverse ensembles for active learning
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
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
Bayesian sparse sampling for on-line reward optimization
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
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Asking the right questions: model-driven optimization using probes
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Active sampling for detecting irrelevant features
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active Learning to Maximize Area Under the ROC Curve
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Approximation algorithms for budgeted learning problems
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Customer targeting models using actively-selected web content
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML '07 Proceedings of the 18th European conference on Machine Learning
Active Learning in Multi-armed Bandits
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Bandit-Based Algorithms for Budgeted Learning
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The ratio index for budgeted learning, with applications
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Active Feature-Value Acquisition
Management Science
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Learning and classifying under hard budgets
ECML'05 Proceedings of the 16th European conference on Machine Learning
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We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples' class labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples, based on algorithms for the multi-armed bandit problem. In addition, we also evaluate a group of algorithms based on the idea of incorporating second-order statistics into decision making. Most of our algorithms are competitive with the current state of art and performed better when the budget was highly limited (in particular, our new algorithm AbsoluteBR2). Finally, we present new heuristics for selecting an instance to purchase after the attribute is selected, instead of selecting an instance uniformly at random, which is typically done. While experimental results showed some performance improvements when using the new instance selectors, there was no consistent winner among these methods.