Abstract
Many machine learning algorithms have been used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.
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Dashevskiy, M., Luo, Z. (2008). Reliable Probabilistic Classification and Its Application to Internet Traffic. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_48
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DOI: https://doi.org/10.1007/978-3-540-87442-3_48
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