Principles of artificial intelligence
Principles of artificial intelligence
Time-dependent utility and action under uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning cost-sensitive active classifiers
Artificial Intelligence
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Sorting and Decision Trees with POMDPs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Decision-theoretic active sensing for autonomous agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Learning to Perform Moderation in Online Forums
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Dynamic Composition of Information Retrieval Techniques
Journal of Intelligent Information Systems
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Attribute Measurement Policies for Time and Cost Sensitive Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning diagnostic policies from examples by systematic search
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Journal of Artificial Intelligence Research
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Decision Support Systems
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Test-cost sensitive classification on data with missing values in the limited time
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
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In time and cost sensitive classification, the utility of labeling an instance depends not only on the correctness of the labeling, but also the amount of time taken to label the instance. Instance attributes are initially unknown, and may take significant time to measure. This results in a difficult problem, trying to manage the tradeoff between time and accuracy. The problem is further complicated when we consider a sequence of time-sensitive classification instances, where time spent measuring attributes in one instance can adversely affect the costs of future instances. We solve these problems using a decision theoretic approach. The problem is modeled as an MDP with a potentially very large state space. We discuss how to intelligently discretize time and approximate the effects of measurement actions in the current instance given all waiting instances. The results offer an effective approach to attribute measurement and classification for a variety of time sensitive applications.