Advertisement

Obtaining Classification Rules Using LVQ+PSO: An Application to Credit Risk

  • Laura Lanzarini
  • Augusto Villa-Monte
  • Aurelio Fernández-Bariviera
  • Patricia Jimbo-Santana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 377)

Abstract

Credit risk management is a key element of financial corporations. One of the main problems that face credit risk officials is to approve or deny a credit petition. The usual decision making process consists in gathering personal and financial information about the borrower. This paper present a new method that is able to generate classifying rules that work no only on numerical attributes, but also on nominal attributes. This method, called LVQ+PSO, combines a competitive neural network with an optimization technique in order to find a reduced set of classifying rules. These rules constitute a predictive model for credit risk approval. Given the reduced quantity of rules, our method is very useful for credit officers aiming to make decisions about granting a credit. Our method was applied to two credit databases that were extensively analyzed by other competing classification methods. We obtain very satisfactory results. Future research lines are exposed.

Keywords

Credit risk Classification rules Learning vector quantization (LVQ) Particle swarm optimization (PSO) 

Notes

Acknowledgments

We thank UCI Machine Learning Repository for the generous provision of data for this paper.

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB ’94, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google Scholar
  2. Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML ’98, pp. 144–151. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
  3. Hernández Orallo, J., Ramírez Quintana, M.J., Ferri Ramírez, C.: Introducción a la Minería de Datos. 1ra Edición, Pearson (2004)Google Scholar
  4. Hung, C., Huang, L.: Extracting rules from optimal clusters of self-organizing maps. ICCMS ’10. Second International Conference on Computer Modeling and Simulation, vol. 1, 2010, pp. 382–386 (2010)Google Scholar
  5. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  6. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108 (1997)Google Scholar
  7. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  8. Kohonen, T., Schroeder, M.R., Huang, T.S. (eds.): Self-organizing Maps, 3rd edn. Springer, New York (2001)zbMATHGoogle Scholar
  9. Lanzarini, L., Lopez, J., Maulini, J.A., Giusti, A.: A new binary pso with velocity control. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) Advances in Swarm Intelligence, Lecture Notes in Computer Science, pp. 111–119. Springer, Heidelberg (2011)Google Scholar
  10. Lanzarini, L., Villa Monte, A., Ronchetti, F.: SOM+PSO. A Novel Method to Obtain Classification Rules. J. Comput. Sci. Technol. (JCS&T) 15(1), (2015) (forthcoming)Google Scholar
  11. Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA. http://archive.ics.uci.edu/ml. Accessed 5 Jan 2015 (2013)
  12. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  13. Venturini, G.S: A supervised inductive algorithm with genetic search for learning attributes based concepts. In: Brazdil, P. (ed.) Machine Learning: ECML-93, Lecture Notes in Computer Science, pp. 280–296. Springer, Heidelberg (1993)Google Scholar
  14. Wang, Z., Sun, X., Zhang, D.: A pso-based classification rule mining algorithm. In: Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, ICIC ’07, pp. 377–384. Springer, Heidelberg (2007)Google Scholar
  15. Witten, I.H., Eibe, F., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Elsevier (2011)Google Scholar
  16. Yu, H., Huang, X., Hu, X., Cai, H.: A comparative study on data mining algorithms for individual credit risk evaluation. In: Fourth International Conference on Management of e-Commerce and e-Government, ICMeCG 2010, pp. 35–38 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Lanzarini
    • 1
  • Augusto Villa-Monte
    • 2
  • Aurelio Fernández-Bariviera
    • 2
  • Patricia Jimbo-Santana
    • 3
  1. 1.Instituto de Investigación en Informática LIDIUniversidad Nacional de la PlataBuenos AiresArgentina
  2. 2.Departament of BusinessUniversitat Rovira i VirgiliReusSpain
  3. 3.Dpto. Ciencias de la ComputaciónESPE Universidad de las Fuerzas Armadas, Campus PolitécnicoSangolquíEcuador

Personalised recommendations