Speeding up algorithm selection using average ranking and active testing by introducing runtime

Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average...

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Vydáno v:	Machine learning Ročník 107; číslo 1; s. 79 - 108
Hlavní autoři:	Abdulrahman, Salisu Mamman, Brazdil, Pavel, van Rijn, Jan N., Vanschoren, Joaquin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2018 Springer Nature B.V
Témata:	Accuracy Algorithms Artificial Intelligence Computer Science Control Machine learning Mechatronics Natural Language Processing (NLP) Ranking Robotics Run time (computers) Simulation and Modeling Special Issue on Metalearning and Algorithm Selection Meta-learning Average ranking Algorithm selection Active testing Mean interval loss Ranking of algorithms Loss curves
ISSN:	0885-6125, 1573-0565
On-line přístup:	Získat plný text
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