[BibTeX] [RIS]
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
Type of publication: Inproceedings
Citation: bohlender20XDCC
Booktitle: Discovery Science - 23rd International Conference, {DS} 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings
Series: Lecture Notes in Computer Science
Volume: 12323
Year: 2020
Month: October
Pages: 471--485
Publisher: Springer International Publishing
Institution: Knowledge Engineering Group, Technische Universität Darmstadt
URL: https://arxiv.org/abs/2006.08094
DOI: 10.1007/978-3-030-61527-7_31
Abstract: Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the concept of dynamic classifier chains (DCC) the label ordering is chosen for each prediction dynamically depending on the respective instance at hand. We combine this concept with the boosting of extreme gradient boosted trees (XGBoost), an effective and scalable state-of-the-art technique, and incorporate DCC in a fast multi-label extension of XGBoost which we make publicly available. As only positive labels have to be predicted and these are usually only few, the training costs can be further substantially reduced. Moreover, as experiments on eleven datasets show, the length of the chain allows for a more control over the usage of previous predictions and hence over the measure one want to optimize.
Authors Bohlender, Simon
Loza MencĂ­a, Eneldo
Kulessa, Moritz