Principles of artificial intelligence
Principles of artificial intelligence
The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision-theoretic troubleshooting
Communications of the ACM
Machine Learning - Special issue on learning with probabilistic representations
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
Robust Classification for Imprecise Environments
Machine Learning
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Data Mining and Knowledge Discovery
An Approximate Nonmyopic Computation for Value of Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning cost-sensitive active classifiers
Artificial Intelligence
Incremental Induction of Decision Trees
Machine Learning
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
BI-POMDP: Bounded, Incremental, Partially-Observable Markov-Model Planning
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Decision graphs: algorithms and applications to influence diagram evaluation and high-level path planning under uncertainty
Learning diagnostic policies from examples by systematic search
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
An instance-based state representation for network repair
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Myopic value of information in influence diagrams
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Optimal testing of structured knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Anytime induction of low-cost, low-error classifiers: a sampling-based approach
Journal of Artificial Intelligence Research
Cost-sensitive classification with unconstrained influence diagrams
SOFSEM'12 Proceedings of the 38th international conference on Current Trends in Theory and Practice of Computer Science
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This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today's desktop computers.