EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization

Detalhes bibliográficos
Autor(a) principal: Swale, Lyinder Nelson
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/152096
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 EasyML: An AutoML System using Meta Learning and Particle Swarm OptimizationAutomated Machine LearningMeta LearningParticle Swarm OptimizationHyperparameter OptimizationOpenMLDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceIn recent years machine learning has made great strides in many application areas and an ever-growing number of disciplines rely on it. However, machine learning modelling process involves trying many machine learning algorithms with different parameter configurations which is considered insufficient, tedious, and time-consuming. The challenge has brought about the need for off-the-shelf solutions that allow a dataset to choose its best modelling pipeline including data preprocessing, model selection and hyperparameter optimization without or with very little human intervention in the process. Despite the availability of numerous AutoML systems that can automate the machine learning modeling process, there is still a need for a solution that can achieve the same results using a significantly smaller space, while improving efficiency. This thesis proposes an AutoML system named EasyML that uses meta-learning for model selection and particle swarm optimization for hyperparameter optimization. The research objectives include conducting a comprehensive literature review on State-of-the-Art techniques and existing AutoML systems, design, and development of EasyML, evaluating the system's performance on benchmark datasets, comparing its efficiency to other AutoML systems, and identifying its limitations and suggesting future research directions. The research methodology combines Design Science Research and CRISP-DM. EasyML outperforms existing solutions like SmartML and Auto-WEKA on all benchmark datasets. EasyML has the potential to contribute to the development of more efficient and effective AutoML systems, thereby meeting the increasing demand for data scientists with strong knowledge of various machine learning algorithms and techniques.Vanneschi, LeonardoRUNSwale, Lyinder Nelson2023-04-24T14:31:14Z2023-04-102023-04-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152096TID:203268431enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:34:27Zoai:run.unl.pt:10362/152096Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:47.722314Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
title EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
spellingShingle EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
Swale, Lyinder Nelson
Automated Machine Learning
Meta Learning
Particle Swarm Optimization
Hyperparameter Optimization
OpenML
title_short EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
title_full EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
title_fullStr EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
title_full_unstemmed EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
title_sort EasyML: An AutoML System using Meta Learning and Particle Swarm Optimization
author Swale, Lyinder Nelson
author_facet Swale, Lyinder Nelson
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Swale, Lyinder Nelson
dc.subject.por.fl_str_mv Automated Machine Learning
Meta Learning
Particle Swarm Optimization
Hyperparameter Optimization
OpenML
topic Automated Machine Learning
Meta Learning
Particle Swarm Optimization
Hyperparameter Optimization
OpenML
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 2023-04-24T14:31:14Z
2023-04-10
2023-04-10T00:00:00Z
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/10362/152096
TID:203268431
url http://hdl.handle.net/10362/152096
identifier_str_mv TID:203268431
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instacron:RCAAP
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