ML-Plan

Automated machine learning via hierarchical planning

authored by

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.

External Organisation(s)

Paderborn University

Type

Article

Journal

Machine learning

Volume

107

Pages

1495-1515

No. of pages

21

ISSN

0885-6125

Publication date

01.09.2018

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Software, Artificial Intelligence

Electronic version(s)

https://doi.org/10.1007/s10994-018-5735-z (Access: Open)