TY  - RPRT
ID  - bohlender20XDCC_arxiv
T1  - Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
A1  - Bohlender, Simon
A1  - Loza Mencía, Eneldo
A1  - Kulessa, Moritz
Y1  - 2020
M1  - ArXiv e-prints
IS  - 2006.08094 [cs.LG]
T2  - Knowledge Engineering Group, Technische Universität Darmstadt
N1  - Preprint of /bibtex/publications/show/3211
UR  - https://arxiv.org/abs/2006.08094
N2  - 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.
M1  - archiveprefix={arXiv}
M1  -  eprint={1908.03032}
M1  -  primaryclass={cs.LG}
ER  -