conference paper 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 2210.17341 L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 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. 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, L., Hanselle, J. M., &#38; 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. @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} } 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. Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection.” <i>Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022</i>, 2022. 341032022-11-17T12:57:40Z2022-11-17T13:00:53Z