Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics

Sašo Džeroski, Hendrik Blockeel, Amanda Clare, Schietgat Leander, Jan Struyf

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

91 Dyfyniadau(SciVal)

Crynodeb

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.
Iaith wreiddiolSaesneg
TeitlKnowledge Discovery in Databases
Is-deitlPKDD 2006
CyhoeddwrSpringer Nature
Tudalennau18-29
Nifer y tudalennau12
ISBN (Argraffiad)978-3-540-45374-1, 978-3-540-46048-0
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2006

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