On the use of semantic awareness to limit overfitting in genetic programming

Detalhes bibliográficos
Autor(a) principal: Englert, Paul Joscha
Data de Publicação: 2017
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/25011
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling On the use of semantic awareness to limit overfitting in genetic programmingMachine LearningStatisticsComputational IntelligenceGenetic ProgrammingGenetic AlgorithmEvolutionary AlgorithmOptimization AlgorithmOptimization ProblemOverfittingSemantic AwarenessDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsMachine learning and statistics provide powerful tools to solving problems of many different shapes. But with the algorithms searching for approximations the problem of overfitting remains present. Genetic Programming describes an algorithmic approach that is likely to produce overfitting solutions. Thus, in order to lessen the risk of overfitting and increasing the generalization ability of genetic programming the use of semantic information is assessed in different ways. A multi-objective system driving the population away from overfitting solutions based on semantic distance is presented alongside alternatives and extensions. The extensions include the use of the semantic signature to increase the amount of information available to the system, as well as the consideration to replace the validation dataset. It is on the one hand concluded that the described approaches and none of the extensions have a positive impact on the generalization ability. But on the other hand it seems that the semantics do contain enough information to appropriately discriminate between overfitting and not overfitting individuals.Vanneschi, LeonardoRUNEnglert, Paul Joscha2017-11-06T15:52:05Z2017-10-272017-10-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/25011TID:201745615enginfo: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:RCAAP2024-03-11T04:13:02Zoai:run.unl.pt:10362/25011Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:28:10.449353Repositório 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 On the use of semantic awareness to limit overfitting in genetic programming
title On the use of semantic awareness to limit overfitting in genetic programming
spellingShingle On the use of semantic awareness to limit overfitting in genetic programming
Englert, Paul Joscha
Machine Learning
Statistics
Computational Intelligence
Genetic Programming
Genetic Algorithm
Evolutionary Algorithm
Optimization Algorithm
Optimization Problem
Overfitting
Semantic Awareness
title_short On the use of semantic awareness to limit overfitting in genetic programming
title_full On the use of semantic awareness to limit overfitting in genetic programming
title_fullStr On the use of semantic awareness to limit overfitting in genetic programming
title_full_unstemmed On the use of semantic awareness to limit overfitting in genetic programming
title_sort On the use of semantic awareness to limit overfitting in genetic programming
author Englert, Paul Joscha
author_facet Englert, Paul Joscha
author_role author
dc.contributor.none.fl_str_mv Vanneschi, Leonardo
RUN
dc.contributor.author.fl_str_mv Englert, Paul Joscha
dc.subject.por.fl_str_mv Machine Learning
Statistics
Computational Intelligence
Genetic Programming
Genetic Algorithm
Evolutionary Algorithm
Optimization Algorithm
Optimization Problem
Overfitting
Semantic Awareness
topic Machine Learning
Statistics
Computational Intelligence
Genetic Programming
Genetic Algorithm
Evolutionary Algorithm
Optimization Algorithm
Optimization Problem
Overfitting
Semantic Awareness
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2017
dc.date.none.fl_str_mv 2017-11-06T15:52:05Z
2017-10-27
2017-10-27T00:00:00Z
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/10362/25011
TID:201745615
url http://hdl.handle.net/10362/25011
identifier_str_mv TID:201745615
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
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