Algorithms for the automated creation of low cost identification keys are described and theoretical and empirical justifications are provided. The algorithms are shown to handle differing test costs, prior probabilities for each potential diagnosis and tests that produce uncertain results. The approach is then extended to cover situations where more than one measure of cost is of importance, by allowing tests to be performed in batches. Experiments are performed on a real-world case study involving the identification of yeasts.
|Title of host publication||Applications of Evolutionary Computing|
|Editors||Stefano Cagnoni, Colin G. Johnson, Juan Cardalda, Elena Marchiori, David Corne, Jean-Arcady Meyer, Jens Gottlieb, Martin Middendorf, Agnès Guillot, Günther Raidl, Emma Hart|
|Number of pages||10|
|Publication status||Published - 2003|
|Name||Lecture Notes in Computer Science|