Ontology-based Meta AutoML

Article type: Research Article

Authors: Zender, Alexander^* | Humm, Bernhard G.

Affiliations: Hochschule Darmstadt – University of Applied Sciences, Darmstadt, Germany

Correspondence: [*] Corresponding author: Alexander Zender, Hochschule Darmstadt – University of Applied Sciences, Darmstadt, Germany. E-mail: alexander.zender@h-da.de.

Abstract: Automated machine learning (AutoML) supports ML engineers and data scientist by automating single tasks like model selection and hyperparameter optimization, automatically generating entire ML pipelines. This article presents a survey of 20 state-of-the-art AutoML solutions, open source and commercial. There is a wide range of functionalities, targeted user groups, support for ML libraries, and degrees of maturity. Depending on the AutoML solution, a user may be locked into one specific ML library technology or one product ecosystem. Additionally, the user might require some expertise in data science and programming for using the AutoML solution. We propose a concept called OMA-ML (Ontology-based Meta AutoML) that combines the features of existing AutoML solutions by integrating them (Meta AutoML). OMA-ML can incorporate any AutoML solution allowing various user groups to generate ML pipelines with the ML library of choice. An ontology is the information backbone of OMA-ML. OMA-ML is being implemented as an open source solution with currently third-party 7 AutoML solutions being integrated.

Keywords: Machine learning, ontology, AutoML, Meta AutoML, OMA-ML

DOI: 10.3233/ICA-220684

Journal: Integrated Computer-Aided Engineering, vol. 29, no. 4, pp. 351-366, 2022