2017/05/27 by Patrick Rodler, Rodler, Patrick, Wolfgang Schmid +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Biomedical Text Mining and Ontologies #FOS: Computer and information sciences #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1705.09879
openalex publication_date 2017/05/27 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
In this work we present strategies for (optimal) measurement selection in\nmodel-based sequential diagnosis. In particular, assuming a set of leading\ndiagnoses being given, we show how queries (sets of measurements) can be\ncomputed and optimized along two dimensions: expected number of queries and\ncost per query. By means of a suitable decoupling of two optimizations and a\nclever search space reduction the computations are done without any inference\nengine calls. For the full search space, we give a method requiring only a\npolynomial number of inferences and guaranteeing query properties existing\nmethods cannot provide. Evaluation results using real-world problems indicate\nthat the new method computes (virtually) optimal queries instantly\nindependently of the size and complexity of the considered diagnosis problems.\n