A diagnostic policy speci es what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeo s between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process (MDP). We investigate two t ypes of algorithms for solving this MDP: systematic search based on the AO algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples, but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed that control over tting and speed up the search. This research is the rst that integrates over tting prevention into systematic search. The paper has two c o n tributions: it discusses the factors that make systematic search feasible for diagnosis, and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.
REF formulates the feature acquisition problem as a Markov Decision Process and provides both greedy and systematic search algorithms to develop diagnostic policies.
Learning Diagnostic Policies from Examples by Systematic Search
arXiv: Artificial Intelligence
Computer Science