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Constructing Bayesian networks to predict uncollectible telecommunications accounts

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2 Author(s)
Ezawa, K.J. ; AT&T Bell Labs., Murray Hill, NJ, USA ; Norton, S.W.

The complexities of building models that can predict whether a customer account or transaction is collectible are greater than most current learning systems can handle. The authors describe software that builds Bayesian network models for such predictions. They also examine how varying model parameters and hence model structure can affect predictive accuracy

Published in:

IEEE Expert  (Volume:11 ,  Issue: 5 )