Abstract
In this paper, a method for telecommunications fraud detection is proposed. The method is based on the user profiling by employing the Latent Dirichlet Allocation (LDA). The detection of fraudulent behavior is achieved with a threshold-type classification algorithm, allocating the telecommunication accounts into one of two classes: fraudulent account and non-fraudulent account. The accounts are classified with use of the Kullback-Leibler divergence (KL-divergence). Therefore, we also introduce four methods for approximating the KL-divergence between two LDAs. Finally, the results of experimental study on KL-divergence approximation and fraud detection in telecommunications are reported.
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Olszewski, D. (2011). Fraud Detection in Telecommunications Using Kullback-Leibler Divergence and Latent Dirichlet Allocation. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_8
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DOI: https://doi.org/10.1007/978-3-642-20267-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20266-7
Online ISBN: 978-3-642-20267-4
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