Efficient AutoML via combinational sampling

Duc Anh Nguyen, Anna V. Kononova, Stefan Menzel, Bernhard Sendhoff, Thomas Back

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Automated machine learning (AutoML) aims to automatically produce the best machine learning pipeline, i.e., a sequence of operators and their optimized hyperparameter settings, to maximize the performance of an arbitrary machine learning problem. Typically, AutoML based Bayesian optimization (BO) approaches convert the AutoML optimization problem into a Hyperparameter Optimization (HPO) problem, where the choice of algorithms is modeled as an additional categorical hyperparameter. In this way, algorithms and their local hyperparameters are referred to as the same level. Consequently, this approach makes the resulting initial sampling less robust. In this study, we describe a first attempt to formulate the AutoML optimization problem as its nature instead of transfer it into a HPO problem. To take advantage of this paradigm, we propose a novel initial sampling approach to maximize the coverage of the AutoML search space to help BO construct a robust surrogate model. We experiment with 2 independent scenarios of AutoML with 2 operators and 6 operators over 117 benchmark datasets. Results of our experiments demonstrate that the performance of BO significantly improved by using our sampling approach.
Original languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781728190488
ISBN (Print)9781728190495 (PoD)
Publication statusPublished - 24 Jan 2022
Externally publishedYes
EventIEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021) - Orlando, United States
Duration: 5 Dec 20217 Dec 2021

Publication series

NameIEEE Symposium Series on Computational Intelligence
ISSN (Print)2770-0097


ConferenceIEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)
Abbreviated titleIEEE SSCI 2021
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 766186 (ECOLE).

Publisher Copyright:
© 2021 IEEE.


  • Machine learning algorithms
  • Computational modeling
  • Pipelines
  • Machine learning
  • Benchmark testing
  • Bayes methods
  • Optimization
  • Initial sampling
  • Robust AutoML

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization
  • Safety, Risk, Reliability and Quality
  • Computer Science Applications


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