%0 Conference Paper %1 reynolds2003algorithms %A Reynolds, Alan P. %A Dicks, Jo L. %A Roberts, Ian N. %A Wesselink, Jan-Jap %A de la Iglesia, Beatriz %A Robert, Vincent %A Boekhout, Teun %A Rayward-Smith, Victor J. %B Applications of Evolutionary Computing %C Berlin %D 2003 %E Cagnoni, Stefano %E Johnson, Colin G. %E Cardalda, Juan J. Romero %E Marchiori, Elena %E Corne, David W. %E Meyer, Jean-Arcady %E Gottlieb, Jens %E Middendorf, Martin %E Guillot, Agnès %E Raidl, Günther R. %E Hart, Emma %I Springer %K 62h30-classification-discrimination-cluster-analysis 68t05-learning-and-adaptive-systems 68t20-problem-solving 68u99-computing-methodologies-and-applications 92b10-taxonomy-cladistics-statistics 92b99-mathematical-biology-in-general %P 107--118 %R 10.1007/3-540-36605-9_11 %T Algorithms for Identification Key Generation and Optimization with Application to Yeast Identification %U https://link.springer.com/chapter/10.1007/3-540-36605-9_11 %V 2611 %X 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. %@ 978-3-540-36605-8

@inproceedings{reynolds2003algorithms, abstract = {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.}, added-at = {2023-04-27T05:00:13.000+0200}, address = {Berlin}, author = {Reynolds, Alan P. and Dicks, Jo L. and Roberts, Ian N. and Wesselink, Jan-Jap and de la Iglesia, Beatriz and Robert, Vincent and Boekhout, Teun and Rayward-Smith, Victor J.}, biburl = {https://www.bibsonomy.org/bibtex/2e64f855234ae72ecc70a058bcb33a312/gdmcbain}, booktitle = {Applications of Evolutionary Computing}, doi = {10.1007/3-540-36605-9_11}, editor = {Cagnoni, Stefano and Johnson, Colin G. and Cardalda, Juan J. Romero and Marchiori, Elena and Corne, David W. and Meyer, Jean-Arcady and Gottlieb, Jens and Middendorf, Martin and Guillot, Agn{\`e}s and Raidl, G{\"u}nther R. and Hart, Emma}, interhash = {48e86a99552deb8a581a0e8ca81c42a0}, intrahash = {e64f855234ae72ecc70a058bcb33a312}, isbn = {978-3-540-36605-8}, keywords = {62h30-classification-discrimination-cluster-analysis 68t05-learning-and-adaptive-systems 68t20-problem-solving 68u99-computing-methodologies-and-applications 92b10-taxonomy-cladistics-statistics 92b99-mathematical-biology-in-general}, pages = {107--118}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2023-04-27T05:00:13.000+0200}, title = {Algorithms for Identification Key Generation and Optimization with Application to Yeast Identification}, url = {https://link.springer.com/chapter/10.1007/3-540-36605-9_11}, volume = 2611, year = 2003 }