A Supervised Machine Learning Approach with Re-training for Unstructured Document Classification in UBE
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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.
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How to Cite
Saini, J. R., & Desai, A. A. (2010). A Supervised Machine Learning Approach with Re-training for Unstructured Document Classification in UBE. INFOCOMP Journal of Computer Science, 9(3), 30–41. Retrieved from https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/310
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