Abstract
In this paper, we study the problem of filtering unsolicited bulk emails, also known as spam emails. We apply a k-NN algorithm with a similarity measure called resemblance and compare it with the naive Bayes and the k-NN algorithm with TF-IDF weighting. Experimental evaluation shows that our method produces the lowest-cost results under different cost models of classification. Compared with TF-IDF weighting, our method is more practical in a dynamic environment. Also, our method successfully catches a notorious class of spams called picospams. We believe that it will be a useful member in a hybrid classifier.
This research was fully supported by a grant from the Research Grants Council of the Hong Kong SAR, China [CityU 1198/03E].
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Chang, M., Poon, C.K. (2005). Catching the Picospams. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_66
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DOI: https://doi.org/10.1007/11425274_66
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