Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification

Detalhes bibliográficos
Autor(a) principal: Frank, Franz Michael
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/149816
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classificationGenetic ProgrammingAutomated Machine LearningImbalanced Binary ClassificationDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe number of available machine learning methods and tools is increasing rapidly, with one recent trend being the usage of advanced genetic programming concepts and automated machine learning tools. However, through the rising number of upcoming innovations, it has become a challenge for machine learning applicants to keep up with all the new opportunities and to identify their potentials. While emerging methods are typically compared to conventional standard machine learning algorithms upon their initial introduction, research is still scarce on comparisons of the performances between the new concepts themselves. Therefore, this thesis provides a comparative analysis of two novel genetic programming techniques, differentiable Cartesian genetic programming for artificial neural networks and geometric semantic genetic programming, alongside three state-of-the-art automated machine learning tools, Auto-Keras, Auto-PyTorch and Auto-sklearn, with regard to their relative performances in the machine learning subfield of imbalanced binary classification. In this analysis, the five methods are tested against each other on 20 benchmark datasets, primarily regarding their average and maximum performance, and subsequently the most successful technique is applied to the real-world problem of fraud detection. The purpose of this thesis is not only to familiarize machine learning users with these methods, but above all to determine whether the novel genetic programming techniques can compete with the more established automated machine learning tools, and to identify the overall best performing method.Bação, Fernando José Ferreira LucasRUNFrank, Franz Michael2024-01-26T01:31:45Z2023-01-262023-01-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149816TID:203239040enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:09:34Zoai:run.unl.pt:10362/149816Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:39:57.885010Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
title Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
spellingShingle Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
Frank, Franz Michael
Genetic Programming
Automated Machine Learning
Imbalanced Binary Classification
title_short Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
title_full Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
title_fullStr Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
title_full_unstemmed Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
title_sort Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
author Frank, Franz Michael
author_facet Frank, Franz Michael
author_role author
dc.contributor.none.fl_str_mv Bação, Fernando José Ferreira Lucas
RUN
dc.contributor.author.fl_str_mv Frank, Franz Michael
dc.subject.por.fl_str_mv Genetic Programming
Automated Machine Learning
Imbalanced Binary Classification
topic Genetic Programming
Automated Machine Learning
Imbalanced Binary Classification
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-01-26
2023-01-26T00:00:00Z
2024-01-26T01:31:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/149816
TID:203239040
url http://hdl.handle.net/10362/149816
identifier_str_mv TID:203239040
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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repository.mail.fl_str_mv info@rcaap.pt
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