Studying elements ofgenetic programming for multiclass classification

Detalhes bibliográficos
Autor(a) principal: Batista, João Eduardo Silva Pombinho
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/35287
Resumo: Tese de mestrado, Engenharia Informática (Interação e Conhecimento) Universidade de Lisboa, Faculdade de Ciências, 2018
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spelling Studying elements ofgenetic programming for multiclass classificationProgramação genéticaAprendizagem automáticaClassificaçãoMulti-classeAglomeração multi-dimensionalTeses de mestrado - 2018Departamento de InformáticaTese de mestrado, Engenharia Informática (Interação e Conhecimento) Universidade de Lisboa, Faculdade de Ciências, 2018Although Genetic Programming (GP) has been very successful in both symbolic regression and binary classification by solving many difficult problems from various domains, it requires improvements in multiclass classification, which due to the high complexity of this kind of problems, requires specialized classifiers. In this project, we explored a multiclass classification GP-based algorithm, the M3GP [4]. The individuals in standard GP only have one node at their root. This means that their output space is in R. Unlike standard GP, M3GP allows each individual to have n nodes at its root. This variation changes the output space to Rn, allowing them to construct clusters of samples and use a cluster-based classification. Although M3GP is capable of creating interpretable models while having competitive results with state-of-the-art classifiers, such as Random Forests and Neural Networks, it has downsides. The focus of this project is to improve the algorithm by exploring two components, the fitness function, and the genetic operators’ selection method. The original fitness function was accuracy-based. Since using this kind of functions does not allow a smooth evolution of the output space, we tried to improve the algorithm by exploring two distance-based fitness functions as an attempt to separate the clusters while bringing the samples closer to their respective centroids. Until now, the genetic operators in M3GP were selected with a fixed probability. Since some operators have a better effect on the fitness at different stages of the evolution, the fixed probabilities allow operators to be selected at the wrong stages of the evolution, slowing down the learning process. In this project, we try to evolve the probability the genetic operators have of being chosen over the generations. On a later stage, we proposed a new crossover genetic operator that uses three individuals for the M3GP algorithm. The results obtained show significantly better results in the training set in half the datasets, while improving the test accuracy in two datasets.Silva, Sara Guilherme Oliveira da, 1972-Repositório da Universidade de LisboaBatista, João Eduardo Silva Pombinho2018-11-06T15:57:54Z201820182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/35287TID:202011747enginfo: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:RCAAP2023-11-08T16:31:09Zoai:repositorio.ul.pt:10451/35287Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:49:46.448772Repositório 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 Studying elements ofgenetic programming for multiclass classification
title Studying elements ofgenetic programming for multiclass classification
spellingShingle Studying elements ofgenetic programming for multiclass classification
Batista, João Eduardo Silva Pombinho
Programação genética
Aprendizagem automática
Classificação
Multi-classe
Aglomeração multi-dimensional
Teses de mestrado - 2018
Departamento de Informática
title_short Studying elements ofgenetic programming for multiclass classification
title_full Studying elements ofgenetic programming for multiclass classification
title_fullStr Studying elements ofgenetic programming for multiclass classification
title_full_unstemmed Studying elements ofgenetic programming for multiclass classification
title_sort Studying elements ofgenetic programming for multiclass classification
author Batista, João Eduardo Silva Pombinho
author_facet Batista, João Eduardo Silva Pombinho
author_role author
dc.contributor.none.fl_str_mv Silva, Sara Guilherme Oliveira da, 1972-
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Batista, João Eduardo Silva Pombinho
dc.subject.por.fl_str_mv Programação genética
Aprendizagem automática
Classificação
Multi-classe
Aglomeração multi-dimensional
Teses de mestrado - 2018
Departamento de Informática
topic Programação genética
Aprendizagem automática
Classificação
Multi-classe
Aglomeração multi-dimensional
Teses de mestrado - 2018
Departamento de Informática
description Tese de mestrado, Engenharia Informática (Interação e Conhecimento) Universidade de Lisboa, Faculdade de Ciências, 2018
publishDate 2018
dc.date.none.fl_str_mv 2018-11-06T15:57:54Z
2018
2018
2018-01-01T00:00:00Z
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/10451/35287
TID:202011747
url http://hdl.handle.net/10451/35287
identifier_str_mv TID:202011747
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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