@inproceedings{4162fd1c526e48b68287ffa90fa0fe74,
title = "Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics",
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.",
author = "Sa{\v s}o D{\v z}eroski and Hendrik Blockeel and Amanda Clare and Schietgat Leander and Jan Struyf",
note = "H. Blockeel, L. Schietgat, J. Struyf, S. Dzeroski, en A. Clare, Decision trees for hierarchical multilabel classification: A case study in functional genomics, Knowledge Discovery in Databases:PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (Fuernkranz, J. and Scheffer, T. and Spiliopoulou, M., eds.), vol 4213, Lecture Notes in Artificial Intelligence, pp. 18-29, 2006 Sponsorship: 1851 Commission",
year = "2006",
doi = "10.1007/11871637_7",
language = "English",
isbn = "3540453741",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "18--29",
booktitle = "Knowledge Discovery in Databases",
address = "Switzerland",
}