Abstract
This paper addresses the problem of predicting several attributes corresponding to telephone users, based on information gathered from the network which defines their communication patterns. Two approaches are compared which are grounded on machine learning techniques: the initial approach makes use of link information between two users, looking for the correlation between user attributes and communication patterns. The second approach exploits the network structure underlying the communication behavior of the user under study. Simulations show that the learning machines are able to extract network information to improve the attribute prediction capabilities.
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Herrera-Yagüe, C., Zufiria, P.J. (2012). Prediction of Telephone User Attributes Based on Network Neighborhood Information. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_50
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DOI: https://doi.org/10.1007/978-3-642-31537-4_50
Publisher Name: Springer, Berlin, Heidelberg
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