Scipediacontent: Scipediacontent moved page Draft Content 396522363 to Machado et al 2020a

2021-01-21T13:24:57Z

Scipediacontent moved page Draft Content 396522363 to Machado et al 2020a

Scipediacontent: Created page with " == Abstract == The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from t..."

2021-01-21T13:24:53Z

Created page with " == Abstract == The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from t..."

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== Abstract ==

The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE - a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient ClassificationPipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.

== Original document ==

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

* [http://arxiv.org/abs/2004.00307 http://arxiv.org/abs/2004.00307]

* [http://arxiv.org/pdf/2004.00307 http://arxiv.org/pdf/2004.00307]

* [http://link.springer.com/content/pdf/10.1007/978-3-030-43722-0_34 http://link.springer.com/content/pdf/10.1007/978-3-030-43722-0_34],
: [http://dx.doi.org/10.1007/978-3-030-43722-0_34 http://dx.doi.org/10.1007/978-3-030-43722-0_34] under the license http://www.springer.com/tdm

* [https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-00307 https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-00307],
: [https://arxiv.org/abs/2004.00307 https://arxiv.org/abs/2004.00307],
: [https://rd.springer.com/chapter/10.1007/978-3-030-43722-0_34 https://rd.springer.com/chapter/10.1007/978-3-030-43722-0_34],
: [https://link.springer.com/chapter/10.1007%2F978-3-030-43722-0_34 https://link.springer.com/chapter/10.1007%2F978-3-030-43722-0_34],
: [https://academic.microsoft.com/#/detail/3015102974 https://academic.microsoft.com/#/detail/3015102974]

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Machado et al 2020a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 396522363 to Machado et al 2020a

Scipediacontent: Created page with " == Abstract == The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from t..."