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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Imprimir: 1064-2315
ISSN On-line: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v41.i10.60
pages 71-80

Comparison of Data Mining Methods while Credit Rating of Natural Persons

Alexander N. Terentyev
Institute of Applied System Analysis of National Technical University of Ukraine "Kiev Polytechnical Institute", Kiev, Ukraine
Petr I. Bidyuk
Institute for Applied System Analysis of National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute", Kiev
Alexandra V. Mironova
National Technical University of Ukraine "Kiev Polytechnic Institute", Kiev, Ukraine
Nikolay Yu. Medin
National Technical University of Ukraine "Kiev Polytechnic Institute", Kiev, Ukraine

RESUMO

The problem of estimating the risks of crediting natural persons by the data mining methods — the cluster analysis, decision trees, artificial neural networks, regression models of discrete choice and Bayesian networks for the purpose of their comparing, is considered. The database of clients of the first branch of the VAB bank is used for constructing the models. The obtained scoring models are estimated by means of the following criteria: common accuracy, errors of the first and second kind. The experimental results, describing the methods and examples of scoring models are given.

Referências

  1. Hunt E.B., Marin J., Stone P., Experiments in induction.

  2. Krivova O.A., Kovalenko A.S., Application of the cluster analysis for identification of the correlation of the demographic development indicators.

  3. Haykin S., Neural networks: a full course.

  4. Riedmiller M. Braun H., A direct adaptive method for faster back propagation learning: the RPROP algorithm.

  5. Terentyev A.N., Bidyuk P.I., Korshevnyuk L.A., Bayesian network as instrument of intelligent data analysis.


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