Abstract
The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution.
Document type: Part of book or chapter of book
Full document
Original document
The different versions of the original document can be found in:
- http://arxiv.org/abs/2001.11158 under the license https://creativecommons.org/licenses/by
- http://dx.doi.org/10.1007/978-3-030-44584-3_28 under the license cc-by
- https://arxiv.org/abs/2001.11158,
- https://link.springer.com/content/pdf/10.1007%2F978-3-030-44584-3_28.pdf,
- https://link.springer.com/chapter/10.1007%2F978-3-030-44584-3_28,
- https://rd.springer.com/chapter/10.1007/978-3-030-44584-3_28,
- https://www.arxiv-vanity.com/papers/2001.11158,
- https://academic.microsoft.com/#/detail/3018814782 under the license https://creativecommons.org/licenses/by/4.0
Document information
Published on 01/01/2020
Volume 2020, 2020
DOI: 10.1007/978-3-030-44584-3_28
Licence: Other
Share this document
claim authorship
Are you one of the authors of this document?