Ensemble methods with non-negative matrix factorization for non-payment prevention system

A well designed and reliable prevention system for non-payment event is very important for the telecom company. Monitoring is especially needed in case the client exceeds the level of his standard payments what can lead to his financial problems. In this paper, we propose a system describing client's behavior and informing about possible problems in advance. In this approach we apply novel ensemble methods to integrate information from many models predicting the customer's behaviour. The ensemble methods base on non-negative matrix factorization which allows identifying the fundamental prediction components. The practical experiment with prevention system confirmed that proposed procedure.

[1]  Yuhong Yang.  Adaptive Regression by Mixing , 2001 .

[2]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[3]  Ryszard Szupiluk,et al.  Prediction Improvement via Smooth Component Analysis and Neural Network Mixing , 2006, ICANN.

[4]  Charles L. Byrne,et al.  Accelerating the EMML algorithm and related iterative algorithms by rescaled block-iterative methods , 1998, IEEE Trans. Image Process..

[5]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[6]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[7]  Andrzej Cichocki,et al.  New Algorithms for Non-Negative Matrix Factorization in Applications to Blind Source Separation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[8]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[9]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[10]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[11]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[12]  Ryszard Szupiluk,et al.  Model Improvement by the Statistical Decomposition , 2004, ICAISC.

[13]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[14]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[15]  S. Amari,et al.  SPARSE COMPONENT ANALYSIS FOR BLIND SOURCE SEPARATION WITH LESS SENSORS THAN SOURCES , 2003 .