Abstract
Combating spam emails is both costly and time consuming. This paper presents a spam classification algorithm that utilizes both majority voting and multiple instance approaches to determine the resulting classification type. By utilizing multiple sub-classifiers, the classifier can be updated by replacing an individual sub-classifier. Furthermore, each sub-classifier represents a small fraction of a typical classifier, so it can be trained in less time with less data as well. The TREC 2007 spam corpus was used to conduct the experiments.
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Moh, TS., Lee, N. (2011). Reducing Classification Times for Email Spam Using Incremental Multiple Instance Classifiers. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_20
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DOI: https://doi.org/10.1007/978-3-642-19423-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19422-1
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