Ensemble methods in ordinal data classification
Autor(a) principal: | |
---|---|
Data de Publicação: | 2014 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/73795 |
Resumo: | Ordinal classification problems can be found in various areas, such as product recommendation systems, intelligent health systems and image recognition. This problems have the goal of learning how to classify certain instances (e.g a movie) in an ordinal scale (e.g. good, average, bad). The performance of supervised learned problems (such as ordinal classification) can be improved by using ensemble methods, where various models are combined to perform better decisions. While there are various ensemble methods for nominal classification, ranking and regression, ordinal classification has not received the same level of attention. The goal of this dissertation is, therefore, to introduce novel ensemble methods for the classification of ordinal data. To do this, first a new ordinal classification algorithm based on decision trees and the data replication method is presented, whose results show that this classifier might perform better than other non-ordinal classifiers. Then, the main ideas of this method are exploited to try and improve ensembles whose models share similarities with decision trees (i.e. AdaBoost.M1 with Decision Stumps and Random Forests). |
id |
RCAP_ca50ea24a0def618683e3d5fb3caa6b8 |
---|---|
oai_identifier_str |
oai:repositorio-aberto.up.pt:10216/73795 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Ensemble methods in ordinal data classificationCiências da computação e da informaçãoComputer and information sciencesOrdinal classification problems can be found in various areas, such as product recommendation systems, intelligent health systems and image recognition. This problems have the goal of learning how to classify certain instances (e.g a movie) in an ordinal scale (e.g. good, average, bad). The performance of supervised learned problems (such as ordinal classification) can be improved by using ensemble methods, where various models are combined to perform better decisions. While there are various ensemble methods for nominal classification, ranking and regression, ordinal classification has not received the same level of attention. The goal of this dissertation is, therefore, to introduce novel ensemble methods for the classification of ordinal data. To do this, first a new ordinal classification algorithm based on decision trees and the data replication method is presented, whose results show that this classifier might perform better than other non-ordinal classifiers. Then, the main ideas of this method are exploited to try and improve ensembles whose models share similarities with decision trees (i.e. AdaBoost.M1 with Decision Stumps and Random Forests).2014-07-142014-07-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/73795TID:201307065engJoão David Pereira da Costainfo: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:RCAAP2023-11-29T15:32:24Zoai:repositorio-aberto.up.pt:10216/73795Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:26:03.653249Repositório 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 |
Ensemble methods in ordinal data classification |
title |
Ensemble methods in ordinal data classification |
spellingShingle |
Ensemble methods in ordinal data classification João David Pereira da Costa Ciências da computação e da informação Computer and information sciences |
title_short |
Ensemble methods in ordinal data classification |
title_full |
Ensemble methods in ordinal data classification |
title_fullStr |
Ensemble methods in ordinal data classification |
title_full_unstemmed |
Ensemble methods in ordinal data classification |
title_sort |
Ensemble methods in ordinal data classification |
author |
João David Pereira da Costa |
author_facet |
João David Pereira da Costa |
author_role |
author |
dc.contributor.author.fl_str_mv |
João David Pereira da Costa |
dc.subject.por.fl_str_mv |
Ciências da computação e da informação Computer and information sciences |
topic |
Ciências da computação e da informação Computer and information sciences |
description |
Ordinal classification problems can be found in various areas, such as product recommendation systems, intelligent health systems and image recognition. This problems have the goal of learning how to classify certain instances (e.g a movie) in an ordinal scale (e.g. good, average, bad). The performance of supervised learned problems (such as ordinal classification) can be improved by using ensemble methods, where various models are combined to perform better decisions. While there are various ensemble methods for nominal classification, ranking and regression, ordinal classification has not received the same level of attention. The goal of this dissertation is, therefore, to introduce novel ensemble methods for the classification of ordinal data. To do this, first a new ordinal classification algorithm based on decision trees and the data replication method is presented, whose results show that this classifier might perform better than other non-ordinal classifiers. Then, the main ideas of this method are exploited to try and improve ensembles whose models share similarities with decision trees (i.e. AdaBoost.M1 with Decision Stumps and Random Forests). |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-07-14 2014-07-14T00:00:00Z |
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 |
https://hdl.handle.net/10216/73795 TID:201307065 |
url |
https://hdl.handle.net/10216/73795 |
identifier_str_mv |
TID:201307065 |
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 |
|
_version_ |
1799136174774681600 |