TY - CONF ID - rapp20boomer T1 - Learning Gradient Boosted Multi-label Classification Rules A1 - Rapp, Michael A1 - Loza Mencía, Eneldo A1 - Fürnkranz, Johannes A1 - Nguyen, Vu-Linh A1 - Hüllermeier, Eyke ED - Hutter, Frank ED - Kersting, Kristian ED - Lijffijt, Jefrey ED - Valera, Isabel TI - Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) T3 - Lecture Notes in Computer Science Y1 - 2020 VL - 12459 SP - 124 EP - 140 PB - Springer UR - https://link.springer.com/chapter/10.1007/978-3-030-67664-3_8 M2 - doi: https://doi.org/10.1007/978-3-030-67664-3_8 KW - Gradient boosting KW - multilabel classification KW - Rule Learning N2 - 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. ER -