Scipediacontent: Scipediacontent moved page Draft Content 738967082 to Nguyen et al 2020a

2021-01-25T10:22:12Z

Scipediacontent moved page Draft Content 738967082 to Nguyen et al 2020a

Scipediacontent: Created page with " == Abstract == The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesia..."

2021-01-25T10:22:09Z

Created page with " == Abstract == The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesia..."

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== 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 ==
<pdf>Media:Draft_Content_738967082-beopen1216-2019-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/2001.11158 http://arxiv.org/abs/2001.11158] under the license https://creativecommons.org/licenses/by

* [https://link.springer.com/content/pdf/10.1007%2F978-3-030-44584-3_28.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-030-44584-3_28.pdf]

* [http://link.springer.com/content/pdf/10.1007/978-3-030-44584-3_28 http://link.springer.com/content/pdf/10.1007/978-3-030-44584-3_28],
: [http://dx.doi.org/10.1007/978-3-030-44584-3_28 http://dx.doi.org/10.1007/978-3-030-44584-3_28] under the license cc-by

* [https://dblp.uni-trier.de/db/journals/corr/corr2001.html#abs-2001-11158 https://dblp.uni-trier.de/db/journals/corr/corr2001.html#abs-2001-11158],
: [https://arxiv.org/abs/2001.11158 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/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://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://rd.springer.com/chapter/10.1007/978-3-030-44584-3_28],
: [https://www.arxiv-vanity.com/papers/2001.11158 https://www.arxiv-vanity.com/papers/2001.11158],
: [https://academic.microsoft.com/#/detail/3018814782 https://academic.microsoft.com/#/detail/3018814782] under the license https://creativecommons.org/licenses/by/4.0

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Scipediacontent: Scipediacontent moved page Draft Content 738967082 to Nguyen et al 2020a

Scipediacontent: Created page with " == Abstract == The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesia..."