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Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/9925

Title: A supervised machine learning approach with re-training for unstructured document classification in UBE
???metadata.dc.creator???: Saini, Jatinderkumar R.
Desai, Apurva A.
Keywords: Unsolicited Bulk Email (UBE)
Unstructured document
Tokenization
Vector Space Document Model (VSDM)
Feature extraction
Supervised machine learning
E-mail não solicitado em massa
Documentos não-estruturados
Tokenização
Modelo de documento de espaço vetorial (VSDM)
Extração de características
Aprendizado automático supervisionado
Publisher: Editora da UFLA
???metadata.dc.date???: 1-Sep-2010
Citation: SAINI, J. R.; DESAI, A. A. A supervised machine learning approach with re-training for unstructured document classification in UBE. INFOCOMP: Journal of Computer Science, Lavras, v. 9, n. 3, p. 30-41, Sept. 2010.
Abstract: Email has become an important means of electronic communication but the viability of its usage is marred by Un-solicited Bulk Email (UBE) messages. UBE poses technical and socio-economic challenges to usage of emails. Besides, the definition and understanding of UBE differs from one person to another. To meet these challenges and combat this menace, we need to understand UBE. Towards this end, this paper proposes a classifier for UBE documents. Technically, this is an application of un-structured document classification using text content analysis and we approach it using supervised machine learning technique. Our experiments show the success rate of proposed classifier is 98.50%. This is the first formal attempt to provide a novel tool for UBE classification and the empirical results show that the tool is strong enough to be implemented in real world.
Other Identifiers: http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/310
???metadata.dc.language???: eng
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