CREDIT SCORING MODEL BASED ON THE AFFINITY SET

Jerzy Michnik, Anna Michnik, Berenika Pietuch

Abstract

The significant development of credit industry led to growing interest in sophisticated methods which can support making more accurate and more rapid credit decisions. The parametric statistical methods such as linear discriminant analysis and logistic regression were soon followed up by nonparametrical methods and other techniques: neural networks, decision trees, and genetic algorithms. This paper investigates the affinity set – a new concept in data mining field. The affinity set model was applied to credit applications database from Poland. The results are compared to those received by Rosetta (the rough sets and genetic algorithm procedure) and logistic regression.

References

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Paper Citation


in Harvard Style

Michnik J., Michnik A. and Pietuch B. (2008). CREDIT SCORING MODEL BASED ON THE AFFINITY SET . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: CIAS, (ICEIS 2008) ISBN 978-989-8111-39-5, pages 287-290. DOI: 10.5220/0001723902870290


in Bibtex Style

@conference{cias08,
author={Jerzy Michnik and Anna Michnik and Berenika Pietuch},
title={CREDIT SCORING MODEL BASED ON THE AFFINITY SET},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: CIAS, (ICEIS 2008)},
year={2008},
pages={287-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001723902870290},
isbn={978-989-8111-39-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: CIAS, (ICEIS 2008)
TI - CREDIT SCORING MODEL BASED ON THE AFFINITY SET
SN - 978-989-8111-39-5
AU - Michnik J.
AU - Michnik A.
AU - Pietuch B.
PY - 2008
SP - 287
EP - 290
DO - 10.5220/0001723902870290