Learning Gradient Boosted Multi-label Classification Rules
| Type of publication: | Inproceedings |
| Citation: | rapp20boomer |
| Booktitle: | Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) |
| Series: | Lecture Notes in Computer Science |
| Volume: | 12459 |
| Year: | 2020 |
| Pages: | 124--140 |
| Publisher: | Springer |
| URL: | https://link.springer.com/chapter/10.1007/978-3-030-67664-3_8 |
| DOI: | https://doi.org/10.1007/978-3-030-67664-3_8 |
| Abstract: | In multi-label classification, where the evaluation of predic-tions is less straightforward than in single-label classification, variousmeaningful, though different, loss functions have been proposed. Ideally,the learning algorithm should be customizable towards a specific choiceof the performance measure. Modern implementations of boosting, mostprominently gradient boosted decision trees, appear to be appealing fromthis point of view. However, they are mostly limited to single-label clas-sification, and hence not amenable to multi-label losses unless these arelabel-wise decomposable. In this work, we develop a generalization of thegradient boosting framework to multi-output problems and propose analgorithm for learning multi-label classification rules that is able to min-imize decomposable as well as non-decomposable loss functions. Usingthe well-known Hamming loss and subset 0/1 loss as representatives, weanalyze the abilities and limitations of our approach on synthetic dataand evaluate its predictive performance on multi-label benchmarks. |
| Keywords: | Gradient boosting, multilabel classification, Rule Learning |
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