Show simple item record Džeroski, Sašo Blockeel, Hendrik Clare, Amanda Leander, Schietgat Struyf, Jan 2008-08-13T16:03:21Z 2008-08-13T16:03:21Z 2006
dc.identifier.citation Džeroski , S , Blockeel , H , Clare , A , Leander , S & Struyf , J 2006 , ' Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics ' pp. 18-29 . en
dc.identifier.other PURE: 73695
dc.identifier.other dspace: 2160/626
dc.identifier.uri en
dc.description H. Blockeel, L. Schietgat, J. Struyf, S. Dzeroski, en A. Clare, Decision trees for hierarchical multilabel classification: A case study in functional genomics, Knowledge Discovery in Databases:PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (Fuernkranz, J. and Scheffer, T. and Spiliopoulou, M., eds.), vol 4213, Lecture Notes in Artificial Intelligence, pp. 18-29, 2006 Sponsorship: 1851 Commission en
dc.description.abstract Hierarchical multilabel classification (HMC) is a variant of classification where instances may belong to multiple classes organized in a hierarchy. The task is relevant for several application domains. This paper presents an empirical study of decision tree approaches to HMC in the area of functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to learning a set of regular classification trees (one for each class). Interestingly, on all 12 datasets we use, the HMC tree wins on all fronts: it is faster to learn and to apply, easier to interpret, and has similar or better predictive performance than the set of regular trees. It turns out that HMC tree learning is more robust to overfitting than regular tree learning. en
dc.format.extent 12 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Non peer reviewed en

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