Abstract
In this paper, we proposed an optimized process neural network based on fourier orthogonal base function, which can deal with both static value and time-varied continuous value simultaneously. To further improve its performance, we optimize the network topological structure, which adopts fourier expansion based preprocessing. Experiments based on the real datasets show that our proposed churn prediction method has better maneuverability and performance. Most important of all, our method has been used in real applications in China Mobile which is the major telecommunication company of the world.
This work is supported by the National Natural Science Foundation of China under Grant No. 60473051 and No.60642004 and IBM and HP Joint Research Project.
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Song, G., Yang, D., Liu, Y., Cui, B., Wu, L., Xie, K. (2007). An Optimized Process Neural Network Model. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_76
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DOI: https://doi.org/10.1007/978-3-540-71703-4_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
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