From Research on Modeling of Uncertain Data: The Case of Small and Medium Enterprises

  • A. Burda
  • Z. S. Hippe
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 98)


A new procedure for combined validation of learning models – built for specifically uncertain data – is briefly described. The procedure, called the queue validation, relies on a combination of resubstitution with the modified learn-and-test paradigm. In the initial experiment [Burda and Hippe 2010] the developed procedure was checked on doubtful (presumably distorted by creative accounting) data, related to small and medium enterprises (further called SME), displaying two concepts: bankrupt or non-bankrupt. In the current research a new set of learning models was generated for the same data using various types of optimized artificial neural networks. All learning models were evaluated using the queue validation methodology. It was found that error rates for bankrupt concept are much larger than error rates for the concept non-bankrupt. It is assumed that this difference in error rates discovered by thequeue validation procedure can be probably used as a hint pointing frauds in the investigated SME data.


Artificial Neural Network Radial Basis Function Learning Model Radial Basis Function Network Uncertain Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • A. Burda
    • 1
  • Z. S. Hippe
    • 2
  1. 1.University of Management and AdministrationZamośćPoland
  2. 2.Institute of Biomedical InformaticsUniversity of Information Technology and ManagementRzeszówPoland

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