Hierarchical Multilabel Classification Task

A Hierarchical Multilabel Classification Task is a Multilabel Classification Task where the Target Classes are organized into a Hierarchy.

• AKA: HMC.
• Context:
  • "Hierarchical multi-label classification (HMC) differs from normal classification in two ways: (1) a single example may belong to multiple classes simultaneously; and (2) the classes are organized in a hierarchy: an example that belongs to some class automatically belongs to all its superclasses (we call this the hierarchy constraint).
  • It can be solved by a Hierarchical Multilabel Classification Algorithm, such as a Hierarchical Decision Tree Algorithm.
  • Is a type of Hierarchical Classification Task.
• See: HMC System.

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References

2008

• (Vens et al., 2008) ⇒ Celine Vens, Jan Struyf, Leander Schietgat, Sašo Džeroski, and Hendrik Blockeel. (2008). “Decision trees for hierarchical multi-label classification." Machine Learning 73(2):185-214, 2008, ISSN 0885-6125, ISI impactfactor 1.742
  • QUOTE: Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy.

2006

• (Blockeel et al., 2006) ⇒ Hendrik Blockeel, Leander Schietgat, Jan Struyf, Sašo Džeroski and Amanda Clare. (2006). “Decision trees for hierarchical multilabel classification: A case study in functional genomics.” In: Johannes Fürnkranz, T. Scheffer and M. Spiliopoulou, editors, Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings. Lecture Notes in Computer Science, volume 4213, pages 18-29, Springer, 2006, ISSN 0302-9743 / ISBN 978-3-540-45374-1. (paper.pdf)
• (Rousu et al., 2006) ⇒ J. Rousu, C. Saunders, S. Szedmak, and John Shawe Taylor (2006). “Kernel-based learning of hierarchical multilabel classification models.” In: Journal of Machine Learning Research, 7, 1601–1626.

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