conference paper
HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection
Lukass
Fehring
author
Jonas Manuel
Hanselle
author 439800000-0002-1231-4985
Alexander
Tornede
author 38209
Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
SFB 901: SFB 901
project
SFB 901 - B: SFB 901 - Project Area B
project
SFB 901 - B2: SFB 901 - Subproject B2
project
It is well known that different algorithms perform differently well on an
instance of an algorithmic problem, motivating algorithm selection (AS): Given
an instance of an algorithmic problem, which is the most suitable algorithm to
solve it? As such, the AS problem has received considerable attention resulting
in various approaches - many of which either solve a regression or ranking
problem under the hood. Although both of these formulations yield very natural
ways to tackle AS, they have considerable weaknesses. On the one hand,
correctly predicting the performance of an algorithm on an instance is a
sufficient, but not a necessary condition to produce a correct ranking over
algorithms and in particular ranking the best algorithm first. On the other
hand, classical ranking approaches often do not account for concrete
performance values available in the training data, but only leverage rankings
composed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon
foreSts - a new algorithm selector leveraging special forests, combining the
strengths of both approaches while alleviating their weaknesses. HARRIS'
decisions are based on a forest model, whose trees are created based on splits
optimized on a hybrid ranking and regression loss function. As our preliminary
experimental study on ASLib shows, HARRIS improves over standard algorithm
selection approaches on some scenarios showing that combining ranking and
regression in trees is indeed promising for AS.
2022Baltimore
eng
Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
2210.17341
Fehring, L., Hanselle, J. M., & Tornede, A. (2022). HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore.
Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection. In: <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>. ; 2022.
@inproceedings{Fehring_Hanselle_Tornede_2022, title={HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}, booktitle={Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}, author={Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}, year={2022} }
L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.
L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, Baltimore, 2022.
Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>, 2022.
Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” In <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>, 2022.
341032022-11-17T12:57:40Z2022-11-17T13:00:53Z