2006 DecisionTreesForHierarchicalMultilabelClassification
- (Blockeel et al., 2006) ⇒ Hendrik Blockeel, Leander Schietgat, Jan Struyf, Sašo Džeroski, Amanda Clare. (2006). “Decision Trees for Hierarchical Multilabel Classification: A case study in functional genomics.” In: Proceedings of 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2006) doi:10.1007/11871637
Subject Headings: Multilabel Classification, Bioinformatics
Notes
- (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
Cited By
Quotes
Abstract
Hierarchical multilabel classification (HMC) is an extension of binary classification where an instance can be labelled with multiple classes that are organised in a hierarchy. A well-known application of this kind of problem is gene function prediction. A gene can have multiple functions at the same time, and these functions are hierarchically organised: a gene predicted to have a certain class should also be predicted to have all its superclasses, as given by the hierarchy. A straightforward approach to solve this problem would be to learn a binary classifier for each class separately and then to combine the predictions. However, this has several disadvantages: (1) learning is not very efficient, since a separate classifier has to be learned for each class, (2) binary classifiers have known problems with skewed class distributions and (3) the hierarchy constraint, implying that a class should be predicted along with all its superclasses, is not automatically fulfilled. The obvious alternative is to learn a single model that predicts all the different classes at once. In this paper we propose a method for learning decision trees that predicts for each instance a set of classes instead of a single class.
References
- [1] H. Blockeel, L. De Raedt, and J. Ramon. Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pages 55–63, 1998.
- [2] Hendrik Blockeel, Leander Schietgat, Jan Struyf, Saˇso Dˇzeroski, and Amanda Clare. Decision trees for hierarchical multilabel classification: A case study in functional genomics. In: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, 2006.
- [3] Leo Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.
- [4] A. Clare. Machine learning and data mining for yeast functional genomics. PhD thesis, University of Wales, Aberystwyth, 2003.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2006 DecisionTreesForHierarchicalMultilabelClassification | Hendrik Blockeel Leander Schietgat Jan Struyf Sašo Džeroski Amanda Clare | Decision Trees for Hierarchical Multilabel Classification: A case study in functional genomics | Proceedings of 10th European Conference on Principles and Practice of Knowledge Discovery in Databases | http://www.cs.kuleuven.be/~jan/papers/HMCBNAIC.pdf | 10.1007/11871637 | 2006 |