Layered TPOT : speeding up tree-based pipeline optimization

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90 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms (AutoML 2017), 10 August 2017, Sydney, Australia
Subtitle of host publicationCollocated with ECMLPKDD 2017
EditorsPavel Brazdil, Joaquin Vanschoren, Frank Hutter, Holger Hoos
PublisherCEUR-WS.org
Pages49-68
Number of pages20
Publication statusPublished - 22 Sep 2017
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 18–22, 2017, Skopje, Macedonia - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201722 Sep 2017
http://ecmlpkdd2017.ijs.si/index.html

Publication series

NameCEUR Workshop Proceedings
Volume1998
ISSN (Print)1613-0073

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 18–22, 2017, Skopje, Macedonia
Abbreviated titleECML PKDD 2017
CountryMacedonia, The Former Yugoslav Republic of
CitySkopje
Period18/09/1722/09/17
Internet address

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