Title: Decision trees for hierarchical multilabel classification: A case study in functional genomics
Authors: Blockeel, Hendrik ×
Schietgat, Leander
Struyf, Jan
Dzeroski, Saso
Clare, Amanda #
Issue Date: 2006
Publisher: Springer
Series Title: Lecture Notes in Computer Science vol:4213 pages:18-29
Conference: 10th European Conference on Principle and Practice of Knowledge Discovery in Databases edition:10 location:Berlin, Germany date:18-22 September 2006
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.
ISBN: 978-3-540-45374-1
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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