General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution

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Title: General-purpose hierarchical optimisation of machine learning pipelines with grammatical evolution
Authors: Estévez-Velarde, Suilan | Gutiérrez, Yoan | Almeida-Cruz, Yudivián | Montoyo, Andres
Research Group/s: Procesamiento del Lenguaje y Sistemas de Información (GPLSI)
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: AutoML | Grammatical evolution | Evolutionary computation | Supervised learning | Natural language processing
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 8-Jan-2021
Publisher: Elsevier
Citation: Information Sciences. 2021, 543: 58-71.
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.
Sponsor: 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).
ISSN: 0020-0255 (Print) | 1872-6291 (Online)
DOI: 10.1016/j.ins.2020.07.035
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2020 Elsevier Inc.
Peer Review: si
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Appears in Collections:INV - GPLSI - Artículos de Revistas

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