Combining models in discrete discriminant analysis
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/12288 |
Resumo: | When conducting discrete discriminant analysis, alternative models provide different levels of predictive accuracy which has encouraged the research in combined models. This research seems to be specially promising when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate the performance of a linear combination of two discrete discriminant analysis models: the first-order independence model and the dependence trees model. The proposed methodology also uses a hierarchical coupling model when addressing multi-class classification problems, decomposing the multi-class problems into several bi-class problems, using a binary tree structure. The analysis is based both on simulated and real datasets. Results of the proposed approach are compared with those obtained by random forests, being generally more accurate. Measures of precision regarding a training set, a test set and cross-validation are presented. The R software is used for the algorithms' implementation. |
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Combining models in discrete discriminant analysisCombining modelsDDADependence trees modelDiscrete discriminant analysisDTMFirst-order independence modelFOIMHierarchical coupling modelHIERMRandom forestRFWhen conducting discrete discriminant analysis, alternative models provide different levels of predictive accuracy which has encouraged the research in combined models. This research seems to be specially promising when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate the performance of a linear combination of two discrete discriminant analysis models: the first-order independence model and the dependence trees model. The proposed methodology also uses a hierarchical coupling model when addressing multi-class classification problems, decomposing the multi-class problems into several bi-class problems, using a binary tree structure. The analysis is based both on simulated and real datasets. Results of the proposed approach are compared with those obtained by random forests, being generally more accurate. Measures of precision regarding a training set, a test set and cross-validation are presented. The R software is used for the algorithms' implementation.Inderscience2016-12-16T14:08:10Z2016-01-01T00:00:00Z20162019-04-22T12:50:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/12288eng1755-805010.1504/IJDATS.2016.077483Marques, A.Ferreira, A. S.Cardoso, M. G. M. S.info: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:RCAAP2023-07-25T17:30:16ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Combining models in discrete discriminant analysis |
title |
Combining models in discrete discriminant analysis |
spellingShingle |
Combining models in discrete discriminant analysis Marques, A. Combining models DDA Dependence trees model Discrete discriminant analysis DTM First-order independence model FOIM Hierarchical coupling model HIERM Random forest RF |
title_short |
Combining models in discrete discriminant analysis |
title_full |
Combining models in discrete discriminant analysis |
title_fullStr |
Combining models in discrete discriminant analysis |
title_full_unstemmed |
Combining models in discrete discriminant analysis |
title_sort |
Combining models in discrete discriminant analysis |
author |
Marques, A. |
author_facet |
Marques, A. Ferreira, A. S. Cardoso, M. G. M. S. |
author_role |
author |
author2 |
Ferreira, A. S. Cardoso, M. G. M. S. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Marques, A. Ferreira, A. S. Cardoso, M. G. M. S. |
dc.subject.por.fl_str_mv |
Combining models DDA Dependence trees model Discrete discriminant analysis DTM First-order independence model FOIM Hierarchical coupling model HIERM Random forest RF |
topic |
Combining models DDA Dependence trees model Discrete discriminant analysis DTM First-order independence model FOIM Hierarchical coupling model HIERM Random forest RF |
description |
When conducting discrete discriminant analysis, alternative models provide different levels of predictive accuracy which has encouraged the research in combined models. This research seems to be specially promising when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate the performance of a linear combination of two discrete discriminant analysis models: the first-order independence model and the dependence trees model. The proposed methodology also uses a hierarchical coupling model when addressing multi-class classification problems, decomposing the multi-class problems into several bi-class problems, using a binary tree structure. The analysis is based both on simulated and real datasets. Results of the proposed approach are compared with those obtained by random forests, being generally more accurate. Measures of precision regarding a training set, a test set and cross-validation are presented. The R software is used for the algorithms' implementation. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-12-16T14:08:10Z 2016-01-01T00:00:00Z 2016 2019-04-22T12:50:11Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/12288 |
url |
http://hdl.handle.net/10071/12288 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1755-8050 10.1504/IJDATS.2016.077483 |
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.publisher.none.fl_str_mv |
Inderscience |
publisher.none.fl_str_mv |
Inderscience |
dc.source.none.fl_str_mv |
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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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1777303970208808960 |