Fraud/Uncollectible Debt Detection Using a Bayesian Network Based Learning System: A Rare Binary Outcome with Mixed Data Structures

The fraud/uncollectible debt problem in the telecommunications industry presents two technical challenges: the detection and the treatment of the account given the detection. In this paper, we focus on the first problem of detection using Bayesian network models, and we briefly discuss the application of a normative expert system for the treatment at the end. We apply Bayesian network models to the problem of fraud/uncollectible debt detection for telecommunication services. In addition to being quite successful at predicting rare event outcomes, it is able to handle a mixture of categorical and continuous data. We present a performance comparison using linear and non-linear discriminant analysis, classification and regression trees, and Bayesian network models.

[1]  Dan Geiger,et al.  An Entropy-based Learning Algorithm of Bayesian Conditional Trees , 1992, UAI.

[2]  David Heckerman,et al.  Probabilistic similarity networks , 1991, Networks.

[3]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[4]  B. Fischhoff,et al.  Behavioral Decision Theory , 1977 .

[5]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[6]  Gregory F. Cooper,et al.  An Entropy-driven System for Construction of Probabilistic Expert Systems from Databases , 1990, UAI.

[7]  Kazuo J. Ezawa.  Value of Evidence on Influence Diagrams , 1994, UAI.

[8]  Eric Horvitz,et al.  Decision Analysis and Expert Systems , 1991, AI Mag..

[9]  James Kelly,et al.  AutoClass: A Bayesian Classification System , 1993, ML.

[10]  Ross D. Shachter.  Evidence Absorption and Propagation through Evidence Reversals , 2013, UAI.

[11]  W J Krzanowski,et al.  Mixtures of continuous and categorical variables in discriminant analysis. , 1980, Biometrics.

[12]  Kristian G. Olesen,et al.  An algebra of bayesian belief universes for knowledge-based systems , 1990, Networks.

[13]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[14]  Gregory M. Provan,et al.  Learning Bayesian Networks Using Feature Selection , 1995, AISTATS.

[15]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine-mediated learning.

[16]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[17]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[18]  W. Krzanowski.  Distance between populations using mixed continuous and categorical variables , 1983 .

[19]  James D. Knoke,et al.  Discrimillant Analysis with Discrete and Continuous Variables , 1982 .

[20]  G. McLachlan.  Discriminant Analysis and Statistical Pattern Recognition , 1992 .

[21]  Calyampudi R. Rao.  Criteria of estimation in large samples , 1965 .

[22]  Pat Langley,et al.  Induction of Selective Bayesian Classifiers , 1994, UAI.

[23]  Kazuo J. Ezawa,et al.  A Bayesian Network Based Learning System: Architecture and Performance Comparison with Other Models , 1995, ECSQARU.

[24]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[25]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[26]  Michael J. Pazzani,et al.  Reducing Misclassification Costs , 1994, ICML.