ML-Plan

Automated machine learning via hierarchical planning

verfasst von

Felix Mohr, Marcel Wever, Eyke Hüllermeier

Abstract

Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.

Externe Organisation(en)

Universität Paderborn

Typ

Artikel

Journal

Machine learning

Band

107

Seiten

1495-1515

Anzahl der Seiten

21

ISSN

0885-6125

Publikationsdatum

01.09.2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Artificial intelligence

Elektronische Version(en)

https://doi.org/10.1007/s10994-018-5735-z (Zugang: Offen)