A model of decision-making with sequential information-acquisition (part 1)
Decision Support Systems
A model of decision-making with sequential information-acquisition (part 2)
Decision Support Systems
Expert systems for configuration at Digital: XCON and beyond
Communications of the ACM
Real-time knowledge-based control systems
Communications of the ACM
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision-theoretic troubleshooting
Communications of the ACM
The base-rate fallacy and the difficulty of intrusion detection
ACM Transactions on Information and System Security (TISSEC)
Sequential Decision Models for Expert System Optimization
IEEE Transactions on Knowledge and Data Engineering
Targeted advertising on the Web with inventory management
Interfaces - Wagner prize papers
Learning cost-sensitive diagnostic policies from data
Learning cost-sensitive diagnostic policies from data
Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
Decision-Centric Active Learning of Binary-Outcome Models
Information Systems Research
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Information Systems Research
When Is the Right Time to Refresh Knowledge Discovered from Data?
Operations Research
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Diagnostic knowledge-based systems are used in a variety of application domains to support classification decisions. The effectiveness of such systems often decreases as the application environment or user preferences change over time. Hence, frequent adjustments to the system knowledge by a human expert become necessary. We study the problem of determining the optimal amount of effort that should be exerted to maintain the system over a planning horizon (finite or infinite). Using the receiver operating characteristic curve to derive a measure for system performance, we maximize system value by balancing system benefits with maintenance costs. The problem is cast as an optimal control model in which the goal is to choose the timing and extent of maintenance that must be expended to maximize system value. We find that the optimal solution usually possesses a steady-state component. The maintenance problem is also solved as a discrete, impulse control problem, as well as one where maintenance effort has a nonlinear impact on system performance.