Extended evaluation of the UPM method for multiclass problems
- Autores
- Ahumada, Hernán César; Grinblat, Guillermo L.; Granitto, Pablo Miguel
- Año de publicación
- 2011
- Idioma
- español castellano
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Multiclass problems are usually of high technological value, but many classification methods are binary in origin. In the last years, several improved solutions based on the combination of simple classifiers were introduced. An interesting solution is based on creating a hierarchy of sub-problems by clustering prototypes of each one of the classes; there- fore the solution is heavily influenced by the label’s information. In this work we analyze a new strategy to solve multiclass problems that makes more use of spatial information than other methods. We construct a hier- archy of subproblems, but opposite to previous developments, based only on spatial information and not using a single prototype for each class. We evaluate the use of different clustering methods (either agglomera- tive or divisive) for this task and also the use two different classifiers (linear SVM and FDA–GenRidge) for each sub-problem (if needed, be- cause in several cases the clustering method directly gives a subset with samples of a single class). We compare the new method with several pre- vious approaches, finding promising results. The good performance of our approach is virtually independent of the classifier coupled to it, which suggest that it success is primarily related to the use of an appropriate clustering strategy.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
UPM
Method for Multiclass Problems - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125236
Ver los metadatos del registro completo
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Extended evaluation of the UPM method for multiclass problemsAhumada, Hernán CésarGrinblat, Guillermo L.Granitto, Pablo MiguelCiencias InformáticasUPMMethod for Multiclass ProblemsMulticlass problems are usually of high technological value, but many classification methods are binary in origin. In the last years, several improved solutions based on the combination of simple classifiers were introduced. An interesting solution is based on creating a hierarchy of sub-problems by clustering prototypes of each one of the classes; there- fore the solution is heavily influenced by the label’s information. In this work we analyze a new strategy to solve multiclass problems that makes more use of spatial information than other methods. We construct a hier- archy of subproblems, but opposite to previous developments, based only on spatial information and not using a single prototype for each class. We evaluate the use of different clustering methods (either agglomera- tive or divisive) for this task and also the use two different classifiers (linear SVM and FDA–GenRidge) for each sub-problem (if needed, be- cause in several cases the clustering method directly gives a subset with samples of a single class). We compare the new method with several pre- vious approaches, finding promising results. The good performance of our approach is virtually independent of the classifier coupled to it, which suggest that it success is primarily related to the use of an appropriate clustering strategy.Sociedad Argentina de Informática e Investigación Operativa2011-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf36-47http://sedici.unlp.edu.ar/handle/10915/125236spainfo:eu-repo/semantics/altIdentifier/issn/1850-2784info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:30:08Zoai:sedici.unlp.edu.ar:10915/125236Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:30:09.187SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Extended evaluation of the UPM method for multiclass problems |
title |
Extended evaluation of the UPM method for multiclass problems |
spellingShingle |
Extended evaluation of the UPM method for multiclass problems Ahumada, Hernán César Ciencias Informáticas UPM Method for Multiclass Problems |
title_short |
Extended evaluation of the UPM method for multiclass problems |
title_full |
Extended evaluation of the UPM method for multiclass problems |
title_fullStr |
Extended evaluation of the UPM method for multiclass problems |
title_full_unstemmed |
Extended evaluation of the UPM method for multiclass problems |
title_sort |
Extended evaluation of the UPM method for multiclass problems |
dc.creator.none.fl_str_mv |
Ahumada, Hernán César Grinblat, Guillermo L. Granitto, Pablo Miguel |
author |
Ahumada, Hernán César |
author_facet |
Ahumada, Hernán César Grinblat, Guillermo L. Granitto, Pablo Miguel |
author_role |
author |
author2 |
Grinblat, Guillermo L. Granitto, Pablo Miguel |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas UPM Method for Multiclass Problems |
topic |
Ciencias Informáticas UPM Method for Multiclass Problems |
dc.description.none.fl_txt_mv |
Multiclass problems are usually of high technological value, but many classification methods are binary in origin. In the last years, several improved solutions based on the combination of simple classifiers were introduced. An interesting solution is based on creating a hierarchy of sub-problems by clustering prototypes of each one of the classes; there- fore the solution is heavily influenced by the label’s information. In this work we analyze a new strategy to solve multiclass problems that makes more use of spatial information than other methods. We construct a hier- archy of subproblems, but opposite to previous developments, based only on spatial information and not using a single prototype for each class. We evaluate the use of different clustering methods (either agglomera- tive or divisive) for this task and also the use two different classifiers (linear SVM and FDA–GenRidge) for each sub-problem (if needed, be- cause in several cases the clustering method directly gives a subset with samples of a single class). We compare the new method with several pre- vious approaches, finding promising results. The good performance of our approach is virtually independent of the classifier coupled to it, which suggest that it success is primarily related to the use of an appropriate clustering strategy. Sociedad Argentina de Informática e Investigación Operativa |
description |
Multiclass problems are usually of high technological value, but many classification methods are binary in origin. In the last years, several improved solutions based on the combination of simple classifiers were introduced. An interesting solution is based on creating a hierarchy of sub-problems by clustering prototypes of each one of the classes; there- fore the solution is heavily influenced by the label’s information. In this work we analyze a new strategy to solve multiclass problems that makes more use of spatial information than other methods. We construct a hier- archy of subproblems, but opposite to previous developments, based only on spatial information and not using a single prototype for each class. We evaluate the use of different clustering methods (either agglomera- tive or divisive) for this task and also the use two different classifiers (linear SVM and FDA–GenRidge) for each sub-problem (if needed, be- cause in several cases the clustering method directly gives a subset with samples of a single class). We compare the new method with several pre- vious approaches, finding promising results. The good performance of our approach is virtually independent of the classifier coupled to it, which suggest that it success is primarily related to the use of an appropriate clustering strategy. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-08 |
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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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