ML-Plan for Unlimited-Length Machine Learning Pipelines

verfasst von

Marcel Wever, Felix Mohr, Eyke Hüllermeier

Abstract

In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated. Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality. More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand. Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline. However, as TPOT follows an evolutionary approach, it suffers
from performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.

Externe Organisation(en)

Universität Paderborn
Heinz Nixdorf Institut (HNI)

Typ

Paper

Anzahl der Seiten

8

Publikationsdatum

07.2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

Elektronische Version(en)

https://ris.uni-paderborn.de/download/3852/3853 (Zugang: Offen)