Layered genetic programming for feature extraction in classification problems

Detalhes bibliográficos
Autor(a) principal: Padolskaitè, Justina
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/113179
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Layered genetic programming for feature extraction in classification problemsGenetic ProgrammingFeature ExtractionDimensionality ReductionClassificationDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsGenetic programming has been proven to be a successful technique for feature extraction in various applications. In this thesis, we present a Layered Genetic Programming system which implements genetic programming-based feature extraction mechanism. The proposed system uses a layered structure where instead of evolving just one population of individuals, several populations are evolved sequentially. Each such population transforms the input data received from the previous population into a lower dimensional space with the aim of improving classification performance. The performance of the proposed system was experimentally tested on 5 real-world problems using different dimensionality reduction step sizes and different classifiers. The proposed method was able to outperform a simple classifier applied directly on the original data on two problems. On the remaining problems, the classifier performed better using the original data. The best solutions were often obtained in the first few layers which implied that increasing the size of the system, i.e. adding more layers was not useful. However, the layered structure allowed control of the size of individuals.Vanneschi, LeonardoBakurov, IllyaRUNPadolskaitè, Justina2021-03-05T19:24:09Z2021-02-252021-02-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113179TID:202662748enginfo: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:RCAAP2024-05-22T17:51:01Zoai:run.unl.pt:10362/113179Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:51:01Repositório 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 Layered genetic programming for feature extraction in classification problems
title Layered genetic programming for feature extraction in classification problems
spellingShingle Layered genetic programming for feature extraction in classification problems
Padolskaitè, Justina
Genetic Programming
Feature Extraction
Dimensionality Reduction
Classification
title_short Layered genetic programming for feature extraction in classification problems
title_full Layered genetic programming for feature extraction in classification problems
title_fullStr Layered genetic programming for feature extraction in classification problems
title_full_unstemmed Layered genetic programming for feature extraction in classification problems
title_sort Layered genetic programming for feature extraction in classification problems
author Padolskaitè, Justina
author_facet Padolskaitè, Justina
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
Bakurov, Illya
RUN
dc.contributor.author.fl_str_mv Padolskaitè, Justina
dc.subject.por.fl_str_mv Genetic Programming
Feature Extraction
Dimensionality Reduction
Classification
topic Genetic Programming
Feature Extraction
Dimensionality Reduction
Classification
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-03-05T19:24:09Z
2021-02-25
2021-02-25T00:00:00Z
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/10362/113179
TID:202662748
url http://hdl.handle.net/10362/113179
identifier_str_mv TID:202662748
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
repository.mail.fl_str_mv mluisa.alvim@gmail.com
_version_ 1817545783992385536

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