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

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

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

91 Citations (SciVal)

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.
Original languageEnglish
Title of host publicationKnowledge Discovery in Databases
Subtitle of host publicationPKDD 2006
PublisherSpringer Nature
Pages18-29
Number of pages12
ISBN (Print)978-3-540-45374-1, 978-3-540-46048-0
DOIs
Publication statusPublished - 2006

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