Layered TPOT : speeding up tree-based pipeline optimization

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)
92 Downloads (Pure)

Samenvatting

With the demand for machine learning increasing, so does the demand for tools which make it easier to use. Automated machine learning (AutoML) tools have been developed to address this need, such as the Tree-Based Pipeline Optimization Tool (TPOT) which uses genetic programming to build optimal pipelines. We introduce Layered TPOT, a modification to TPOT which aims to create pipelines equally good as the original, but in significantly less time. This approach evaluates candidate pipelines on increasingly large subsets of the data according to their fitness, using a modified evolutionary algorithm to allow for separate competition between pipelines trained on different sample sizes. Empirical evaluation shows that, on sufficiently large datasets, Layered TPOT indeed finds better models faster.
Originele taal-2Engels
TitelProceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms (AutoML 2017), 10 August 2017, Sydney, Australia
SubtitelCollocated with ECMLPKDD 2017
RedacteurenPavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger Hoos
UitgeverijCEUR-WS.org
Pagina's49-68
Aantal pagina's20
StatusGepubliceerd - 22 sep 2017
EvenementEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 18–22, 2017, Skopje, Macedonia - Skopje, Macedonië
Duur: 18 sep 201722 sep 2017
http://ecmlpkdd2017.ijs.si/index.html

Publicatie series

NaamCEUR Workshop Proceedings
Volume1998
ISSN van geprinte versie1613-0073

Congres

CongresEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 18–22, 2017, Skopje, Macedonia
Verkorte titelECML PKDD 2017
LandMacedonië
StadSkopje
Periode18/09/1722/09/17
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