Library Subscription: Guest
Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections
Journal of Automation and Information Sciences
SJR: 0.238 SNIP: 0.464 CiteScore™: 0.27

ISSN Print: 1064-2315
ISSN Online: 2163-9337

Volumes:
Volume 50, 2018 Volume 49, 2017 Volume 48, 2016 Volume 47, 2015 Volume 46, 2014 Volume 45, 2013 Volume 44, 2012 Volume 43, 2011 Volume 42, 2010 Volume 41, 2009 Volume 40, 2008 Volume 39, 2007 Volume 38, 2006 Volume 37, 2005 Volume 36, 2004 Volume 35, 2003 Volume 34, 2002 Volume 33, 2001 Volume 32, 2000 Volume 31, 1999 Volume 30, 1998 Volume 29, 1997 Volume 28, 1996

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

ABSTRACT

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.

REFERENCES

  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.