Consolidated trees versus bagging when explanation is required

In some real-world problems solved by machine learning it is compulsory for the solution provided to be comprehensible so that the correct decision can be made. It is in this context that this paper compares bagging (one of the most widely used multiple classifier systems) with the consolidated tree...

Descripción completa

Detalles Bibliográficos
Autores: Pérez de la Fuente, Jesús María, Albisua Goñi, Iñaki, Arbelaiz Gallego, Olatz, Gurrutxaga Goikoetxea, Ibai, Martín Aramburu, José Ignacio, Muguerza Rivero, Javier Francisco, Perona Balda, Iñigo
Tipo de recurso: artículo
Fecha de publicación:2010
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/71478
Acceso en línea:http://hdl.handle.net/10810/71478
Access Level:acceso abierto
Palabra clave:machine learning
multiple classifier systems
bagging
decision trees
comprehensibility
id ES_a406feb92b9dbbe01d0d5006d4ceea4b
oai_identifier_str oai:addi.ehu.eus:10810/71478
network_acronym_str ES
network_name_str España
repository_id_str
spelling Consolidated trees versus bagging when explanation is requiredPérez de la Fuente, Jesús MaríaAlbisua Goñi, IñakiArbelaiz Gallego, OlatzGurrutxaga Goikoetxea, IbaiMartín Aramburu, José IgnacioMuguerza Rivero, Javier FranciscoPerona Balda, Iñigomachine learningmultiple classifier systemsbaggingdecision treescomprehensibilityIn some real-world problems solved by machine learning it is compulsory for the solution provided to be comprehensible so that the correct decision can be made. It is in this context that this paper compares bagging (one of the most widely used multiple classifier systems) with the consolidated trees construction (CTC) algorithm, when the learning problem to be solved requires the classification made to be provided with an explanation. Bearing in mind the comprehensibility shortcomings of bagging, the Domingos' proposal, called combining multiple models, has been used to address this problem. The two algorithms have been compared from three main points of view: accuracy, quality of the explanation the classification is provided with, and computational cost. The results obtained show that it is beneficial to use CTC in situations where an explanation is required, because: CTC has a greater discriminating capacity than the explanation extraction algorithm added to bagging; the explanation provided is of a greater quality; it is simpler and more reliable; and CTC is computationally more efficient.Springer202520252010info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/71478reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://doi.org/10.1007/s00607-010-0094-zinfo:eu-repo/semantics/openAccess© 2010, Springer-Verlagoai:addi.ehu.eus:10810/714782026-06-18T09:23:17Z
dc.title.none.fl_str_mv Consolidated trees versus bagging when explanation is required
title Consolidated trees versus bagging when explanation is required
spellingShingle Consolidated trees versus bagging when explanation is required
Pérez de la Fuente, Jesús María
machine learning
multiple classifier systems
bagging
decision trees
comprehensibility
title_short Consolidated trees versus bagging when explanation is required
title_full Consolidated trees versus bagging when explanation is required
title_fullStr Consolidated trees versus bagging when explanation is required
title_full_unstemmed Consolidated trees versus bagging when explanation is required
title_sort Consolidated trees versus bagging when explanation is required
dc.creator.none.fl_str_mv Pérez de la Fuente, Jesús María
Albisua Goñi, Iñaki
Arbelaiz Gallego, Olatz
Gurrutxaga Goikoetxea, Ibai
Martín Aramburu, José Ignacio
Muguerza Rivero, Javier Francisco
Perona Balda, Iñigo
author Pérez de la Fuente, Jesús María
author_facet Pérez de la Fuente, Jesús María
Albisua Goñi, Iñaki
Arbelaiz Gallego, Olatz
Gurrutxaga Goikoetxea, Ibai
Martín Aramburu, José Ignacio
Muguerza Rivero, Javier Francisco
Perona Balda, Iñigo
author_role author
author2 Albisua Goñi, Iñaki
Arbelaiz Gallego, Olatz
Gurrutxaga Goikoetxea, Ibai
Martín Aramburu, José Ignacio
Muguerza Rivero, Javier Francisco
Perona Balda, Iñigo
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv machine learning
multiple classifier systems
bagging
decision trees
comprehensibility
topic machine learning
multiple classifier systems
bagging
decision trees
comprehensibility
description In some real-world problems solved by machine learning it is compulsory for the solution provided to be comprehensible so that the correct decision can be made. It is in this context that this paper compares bagging (one of the most widely used multiple classifier systems) with the consolidated trees construction (CTC) algorithm, when the learning problem to be solved requires the classification made to be provided with an explanation. Bearing in mind the comprehensibility shortcomings of bagging, the Domingos' proposal, called combining multiple models, has been used to address this problem. The two algorithms have been compared from three main points of view: accuracy, quality of the explanation the classification is provided with, and computational cost. The results obtained show that it is beneficial to use CTC in situations where an explanation is required, because: CTC has a greater discriminating capacity than the explanation extraction algorithm added to bagging; the explanation provided is of a greater quality; it is simpler and more reliable; and CTC is computationally more efficient.
publishDate 2010
dc.date.none.fl_str_mv 2010
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/71478
url http://hdl.handle.net/10810/71478
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1007/s00607-010-0094-z
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
© 2010, Springer-Verlag
eu_rights_str_mv openAccess
rights_invalid_str_mv © 2010, Springer-Verlag
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869415446163226624
score 15,811543

Similares en LA Referencia