---
res:
bibo_abstract:
- "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.@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Lukass
foaf_name: Fehring, Lukass
foaf_surname: Fehring
- foaf_Person:
foaf_givenName: Jonas Manuel
foaf_name: Hanselle, Jonas Manuel
foaf_surname: Hanselle
foaf_workInfoHomepage: http://www.librecat.org/personId=43980
orcid: 0000-0002-1231-4985
- foaf_Person:
foaf_givenName: Alexander
foaf_name: Tornede, Alexander
foaf_surname: Tornede
foaf_workInfoHomepage: http://www.librecat.org/personId=38209
dct_date: 2022^xs_gYear
dct_language: eng
dct_title: 'HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection@'
...