Scipediacontent: Scipediacontent moved page Draft Content 783418769 to Montoyo et al 2020a

2021-02-16T12:46:09Z

Scipediacontent moved page Draft Content 783418769 to Montoyo et al 2020a

Scipediacontent: Created page with " == Abstract == This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt..."

2021-02-16T12:46:06Z

Created page with " == Abstract == This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt..."

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== Abstract ==

This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt has been designed to provide a flexible framework where a researcher can define the space of possible pipelines to solve a specific machine learning problem, which can range from high-level decisions about representation and features to low-level hyper-parameter values. The evaluation of HML-Opt is presented via two different case studies, both of which demonstrate that it is competitive with existing AutoML tools on a variety of benchmarks. Furthermore, HML-Opt can be applied to novel problems, such as knowledge extraction from natural language text, whereas other techniques are insufficiently flexible to capture the complexity of these scenarios. The source code for HML-Opt is available online for the research community. This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University of Havana. Moreover, it has also been partially funded by both aforementioned universities, the Generalitat Valenciana (Conselleria d’Educació, Investigació, Cultura i Esport) and the Spanish Government through the projects LIVING-LANG (RTI2018-094653-B-C22) and SIIA (PROMETEO/2018/089, PROMETEU/2018/089).

== Original document ==

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

* [http://hdl.handle.net/10045/108428 http://hdl.handle.net/10045/108428]

* [https://api.elsevier.com/content/article/PII:S0020025520306988?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0020025520306988?httpAccept=text/xml],
: [https://api.elsevier.com/content/article/PII:S0020025520306988?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S0020025520306988?httpAccept=text/plain],
: [http://dx.doi.org/10.1016/j.ins.2020.07.035 http://dx.doi.org/10.1016/j.ins.2020.07.035] under the license https://www.elsevier.com/tdm/userlicense/1.0/

* [https://www.sciencedirect.com/science/article/pii/S0020025520306988 https://www.sciencedirect.com/science/article/pii/S0020025520306988],
: [http://rua.ua.es/dspace/handle/10045/108428 http://rua.ua.es/dspace/handle/10045/108428],
: [https://academic.microsoft.com/#/detail/3044145829 https://academic.microsoft.com/#/detail/3044145829]

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Montoyo et al 2020a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 783418769 to Montoyo et al 2020a

Scipediacontent: Created page with " == Abstract == This paper introduces Hierarchical Machine Learning Optimisation (HML-Opt), an AutoML framework that is based on probabilistic grammatical evolution. HML-Opt..."