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Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning

verfasst von
Felix Mohr, Marcel Wever, Alexander Tornede, Eyke Hüllermeier
Abstract

Automated machine learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoMLis an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout. In this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions.

Externe Organisation(en)
Universität Paderborn
Typ
Artikel
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Band
43
Seiten
3055-3066
Anzahl der Seiten
12
ISSN
0162-8828
Publikationsdatum
01.09.2021
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Software, Maschinelles Sehen und Mustererkennung, Theoretische Informatik und Mathematik, Artificial intelligence, Angewandte Mathematik
Elektronische Version(en)
https://doi.org/10.1109/tpami.2021.3056950 (Zugang: Geschlossen)