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dc.contributor.authorKramer, Stefan
dc.contributor.authorWidmer, Gerhard
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorde Groeve, Michael
dc.coverage.spatialConference held at Charlotte, North Carolina, USen_NZ
dc.identifier.citationKramer, S., Widmer, G. Pfahringer, B. & DeGroeve, M. (2000). Prediction of ordinal classes using regression tree. In Z.W. Ras & S. Ohsuga(Eds), Proceedings of 12th International Symposium, ISMIS 2000 Charlotte, NC, USA, October 11–14, 2000. (pp. 665-674). Berlin: Springer.en_US
dc.description.abstractThis paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.en_US
dc.publisherSpringer, Berlinen_US
dc.source12th International Symposium on Methodologies for Intelligent Systems (ISMIS)en_NZ
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titlePrediction of ordinal classes using regression treesen_US
dc.typeConference Contributionen_US
dc.relation.isPartOf12th International Symposium on Methodologies for Intelligent Systemsen_NZ
pubs.volumeProceedings of 12th Int. Symp. on Methodologies for Intelligent Systemsen_NZ

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