Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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

34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th