The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scan line methods for displaying parametrically defined surfaces
Communications of the ACM
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic cognitive style identification of digital library users for personalization
Journal of the American Society for Information Science and Technology
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
The hidden image of the city: sensing community well-being from urban mobility
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
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The socioeconomic status of a population or an individual provides an understanding of its access to housing, education, health or basic services like water and electricity. In itself, it is also an indirect indicator of the purchasing power and as such a key element when personalizing the interaction with a customer, especially for marketing campaigns or offers of new products. In this paper we study if the information derived from the aggregated use of cell phone records can be used to identify the socioeconomic levels of a population. We present predictive models constructed with SVMs and Random Forests that use the aggregated behavioral variables of the communication antennas to predict socioeconomic levels. Our results show correct prediction rates of over 80% for an urban population of around 500,000 citizens.