Abstract
Spam mail classification and filtering is a commonly investigated problem, yet there has been little research into the application of nearest neighbour classifiers in this field. This paper examines the possibility of using a nearest neighbour algorithm for simple, word based spam mail classification. This approach is compared to a neural network, and decision-tree along with results published in another conference paper on the subject.
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© 2004 Springer-Verlag Berlin Heidelberg
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Trudgian, D.C. (2004). Spam Classification Using Nearest Neighbour Techniques. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_85
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DOI: https://doi.org/10.1007/978-3-540-28651-6_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
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