Algorithm selection via meta-learning and sample-based active testing

S.M. Abdulrahman, P. Brazdil, J.N. van Rijn, J. Vanschoren

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

6 Citaten (Scopus)
2 Downloads (Pure)

Samenvatting

Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection. Keywords: Algorithm selection, Meta-learning, Active testing, Algorithm Ranking
Originele taal-2Engels
TitelInternational Workshop on Meta-Learning and Algorithm Selection (MetaSel 2015, Porto, Portugal September 7, 2015; co-located with ECMLPKDD 2015)
RedacteurenJ. Vanschoren, P. Brazdil, C. Giraud-Carrier, L. Kotthoff
UitgeverijCEUR-WS.org
Pagina's55-66
StatusGepubliceerd - 2015

Publicatie series

NaamCEUR Workshop Proceedings
Volume1455
ISSN van geprinte versie1613-0073

    Vingerafdruk

Citeer dit

Abdulrahman, S. M., Brazdil, P., van Rijn, J. N., & Vanschoren, J. (2015). Algorithm selection via meta-learning and sample-based active testing. In J. Vanschoren, P. Brazdil, C. Giraud-Carrier, & L. Kotthoff (editors), International Workshop on Meta-Learning and Algorithm Selection (MetaSel 2015, Porto, Portugal September 7, 2015; co-located with ECMLPKDD 2015) (blz. 55-66). (CEUR Workshop Proceedings; Vol. 1455). CEUR-WS.org.