---
_id: '34103'
abstract:
- lang: eng
text: "It is well known that different algorithms perform differently well on an\r\ninstance
of an algorithmic problem, motivating algorithm selection (AS): Given\r\nan instance
of an algorithmic problem, which is the most suitable algorithm to\r\nsolve it?
As such, the AS problem has received considerable attention resulting\r\nin various
approaches - many of which either solve a regression or ranking\r\nproblem under
the hood. Although both of these formulations yield very natural\r\nways to tackle
AS, they have considerable weaknesses. On the one hand,\r\ncorrectly predicting
the performance of an algorithm on an instance is a\r\nsufficient, but not a necessary
condition to produce a correct ranking over\r\nalgorithms and in particular ranking
the best algorithm first. On the other\r\nhand, classical ranking approaches often
do not account for concrete\r\nperformance values available in the training data,
but only leverage rankings\r\ncomposed from such data. We propose HARRIS- Hybrid
rAnking and RegRessIon\r\nforeSts - a new algorithm selector leveraging special
forests, combining the\r\nstrengths of both approaches while alleviating their
weaknesses. HARRIS'\r\ndecisions are based on a forest model, whose trees are
created based on splits\r\noptimized on a hybrid ranking and regression loss function.
As our preliminary\r\nexperimental study on ASLib shows, HARRIS improves over
standard algorithm\r\nselection approaches on some scenarios showing that combining
ranking and\r\nregression in trees is indeed promising for AS."
author:
- first_name: Lukass
full_name: Fehring, Lukass
last_name: Fehring
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
citation:
ama: 'Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests
for Algorithm Selection. In: *Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS
2022*. ; 2022.'
apa: 'Fehring, L., Hanselle, J. M., & Tornede, A. (2022). HARRIS: Hybrid Ranking
and Regression Forests for Algorithm Selection. *Workshop on Meta-Learning (MetaLearn
2022) @ NeurIPS 2022*. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS
2022, Baltimore.'
bibtex: '@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} }'
chicago: 'Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS:
Hybrid Ranking and Regression Forests for Algorithm Selection.” In *Workshop
on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022*, 2022.'
ieee: '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.'
mla: 'Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for
Algorithm Selection.” *Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS
2022*, 2022.'
short: 'L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn
2022) @ NeurIPS 2022, 2022.'
conference:
location: Baltimore
name: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
date_created: 2022-11-17T12:57:40Z
date_updated: 2022-11-17T13:00:53Z
external_id:
arxiv:
- '2210.17341'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
status: public
title: 'HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection'
type: conference
user_id: '38209'
year: '2022'
...