2020
DOI: 10.1007/978-3-030-43722-0_34
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Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

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 aut… Show more

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Cited by 11 publications
(6 citation statements)
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“…Finally, from a computational point-of-view, interesting future lines of research regard: the use of semantic features [75,76] for the task analyzed in this article, the automatic augmentation of a text dataset used, not for semantic, but syntactic and linguistic tasks, and the use of evolutionary search techniques (such as [77][78][79][80]) for automatically tuning the classification engine without any manual intervention.…”
Section: Discussionmentioning
confidence: 99%
“…Finally, from a computational point-of-view, interesting future lines of research regard: the use of semantic features [75,76] for the task analyzed in this article, the automatic augmentation of a text dataset used, not for semantic, but syntactic and linguistic tasks, and the use of evolutionary search techniques (such as [77][78][79][80]) for automatically tuning the classification engine without any manual intervention.…”
Section: Discussionmentioning
confidence: 99%
“…A pipeline in the context of ML can be described as a utility method that allows the design of a procedure from the data preprocessing to the training of the classifier offering some advantages over the manual execution of these steps. The purpose of the pipeline is to assemble the above methods that can be cross-validated together while setting different parameters in the context of using the Scikit-learn library [55]. The pipeline method eventually implements the solution for avoiding data leakage.…”
Section: Resultsmentioning
confidence: 99%
“…Moreover, some researchers have employed grammars to define the structure of a valid workflow, enabling them to avoid, for example, the application of a neural network to a dataset with categorical features. Both G3P [23,22] and grammatical evolution [43,44] have been applied to guide the optimisation process. However, their grammars still impose restrictions on the workflow structure.…”
Section: Review Workmentioning
confidence: 99%