Learning to detect spam messages
- Autores
- Gil Costa, Graciela Verónica; Errecalde, Marcelo Luis; Taranilla, María Teresa
- Año de publicación
- 2005
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.
VI Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Electronic mail
Message sending
Information filtering
spam
anti-spam filtering
automated text categorization
machine learning
k-NN - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22957
Ver los metadatos del registro completo
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Learning to detect spam messagesGil Costa, Graciela VerónicaErrecalde, Marcelo LuisTaranilla, María TeresaCiencias InformáticasElectronic mailMessage sendingInformation filteringspamanti-spam filteringautomated text categorizationmachine learningk-NNThe problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2005-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/22957enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:16Zoai:sedici.unlp.edu.ar:10915/22957Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:16.902SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Learning to detect spam messages |
title |
Learning to detect spam messages |
spellingShingle |
Learning to detect spam messages Gil Costa, Graciela Verónica Ciencias Informáticas Electronic mail Message sending Information filtering spam anti-spam filtering automated text categorization machine learning k-NN |
title_short |
Learning to detect spam messages |
title_full |
Learning to detect spam messages |
title_fullStr |
Learning to detect spam messages |
title_full_unstemmed |
Learning to detect spam messages |
title_sort |
Learning to detect spam messages |
dc.creator.none.fl_str_mv |
Gil Costa, Graciela Verónica Errecalde, Marcelo Luis Taranilla, María Teresa |
author |
Gil Costa, Graciela Verónica |
author_facet |
Gil Costa, Graciela Verónica Errecalde, Marcelo Luis Taranilla, María Teresa |
author_role |
author |
author2 |
Errecalde, Marcelo Luis Taranilla, María Teresa |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Electronic mail Message sending Information filtering spam anti-spam filtering automated text categorization machine learning k-NN |
topic |
Ciencias Informáticas Electronic mail Message sending Information filtering spam anti-spam filtering automated text categorization machine learning k-NN |
dc.description.none.fl_txt_mv |
The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts. VI Workshop de Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
The problem of unwanted e-mails (or spam messages) has been increasing for years. Different methods have been proposed in order to deal with this problem wich includes blacklists of known spammers, handcrafted rules and machine learning techniques. In this paper we investigate the performance of the k Nearest Neighbours (k-NN) method in spam detection tasks. At this end, a number of different document codifications were tested. Moreover, we study how the vocabulary size reduction affects this task. In the experimental design, different k values were considered and results were analyzed with respect to a public mailing list and personal e-mail collections. The experiments showed that results with public mailing lists tend to be very optimistic and they should not be considered representative of those expected with personal user accounts. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005-10 |
dc.type.none.fl_str_mv |
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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eng |
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eng |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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