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  -