FUZZY CLASSIFIER BASED ON SUPERVISED CLUSTERING WITH NONPARAMETRIC ESTIMATION OF LOCAL PROBABILISTIC DENSITIES IN DEFAULT PREDICTION OF SMALL ENTERPRISES

Maria Luiza F. Velloso, Nival N. Almeida, Thales Ávila Carneiro, José Augusto Gonçalves do Canto

Abstract

The accuracy-complexity trade-off has been an important issue in system modeling. Parsimonious modelling is preferred to complex modelling and, of course, accurate modelling is preferred to inaccurate modelling. In system modelling with fuzzy rule-based systems, the accuracy-complexity tradeoff is often referred as the interpretability-accuracy trade-off, and high interpretability is the main advantage of fuzzy rule-based systems over other nonlinear systems. In many applications, gaining knowledge about the system, in an understandable way, is as important as getting accurate results. The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper we use a fuzzy model structure where each rule represents more than one class with different probabilities. The rules are extracted through clustering and the probabilities are estimated in a local (cluster by cluster) non-parametric way. This approach is applied to predict default in small and medium enterprises in Brazil, using indexes that reflect the financial situation of enterprise, such as profitable capability, operating efficiency, repayment capability and situation of enterprise’s cash flow. The preliminary results show a significant improvement in the interpretability, without accuracy loss, compared with other approaches.

References

  1. Abonyi, J, Szeifert, F., 2003. Supervised fuzzy clustering for the identification of fuzzy classifiers, Pattern Recognition Letters 24, pages 2195-2207
  2. Altman1, Edward I., Sabato, Gabriele, 2007. Modelling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, Volume 43, Issue 3, pages 332-357.
  3. Behr, P. and Güttler, A. (2007), Credit Risk Assessment and Relationship Lending: An Empirical Analysis of German Small and Medium-Sized Enterprises. Journal of Small Business Management, 45, pages 194-213.
  4. Bishop, C. M., 1995. Neural Networks for Pattern Recognition. Oxford University Press, Oxford.
  5. Casillas, J.; Cordón, O.; Herrera Triguero, F.; Magdalena, L. (Eds.), 2003. Accuracy Improvements in Linguistic Fuzzy Modelling Series: Studies in Fuzziness and Soft Computing, Vol. 129 , Springer.
  6. Gath, I., Geva, A.B., 1989. Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Machine Intell 7, pages 773-781.
  7. Lee, Hyong-Euk, Park, Kwang-Hyun, and Bien, Zeungnam Zenn, 2008. Iterative Fuzzy Clustering Algorithm With Supervision to Construct Probabilistic Fuzzy Rule Base From Numerical Data, IEEE Transactions On Fuzzy Systems, Vol. 16, No. 1, pages 263- 277.
  8. Roubos, Johannes A, Setnes, Magne, Abonyi, Janos, 2003. Learning fuzzy classification rules from labeled data. Information Sciences-Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing Volume 150 Issue 1-2, Publisher Elsevier Science Inc. New York, NY, USA
Download


Paper Citation


in Harvard Style

F. Velloso M., N. Almeida N., Ávila Carneiro T. and Augusto Gonçalves do Canto J. (2011). FUZZY CLASSIFIER BASED ON SUPERVISED CLUSTERING WITH NONPARAMETRIC ESTIMATION OF LOCAL PROBABILISTIC DENSITIES IN DEFAULT PREDICTION OF SMALL ENTERPRISES . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 509-512. DOI: 10.5220/0003675305090512


in Bibtex Style

@conference{fcta11,
author={Maria Luiza F. Velloso and Nival N. Almeida and Thales Ávila Carneiro and José Augusto Gonçalves do Canto},
title={FUZZY CLASSIFIER BASED ON SUPERVISED CLUSTERING WITH NONPARAMETRIC ESTIMATION OF LOCAL PROBABILISTIC DENSITIES IN DEFAULT PREDICTION OF SMALL ENTERPRISES},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},
year={2011},
pages={509-512},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003675305090512},
isbn={978-989-8425-83-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
TI - FUZZY CLASSIFIER BASED ON SUPERVISED CLUSTERING WITH NONPARAMETRIC ESTIMATION OF LOCAL PROBABILISTIC DENSITIES IN DEFAULT PREDICTION OF SMALL ENTERPRISES
SN - 978-989-8425-83-6
AU - F. Velloso M.
AU - N. Almeida N.
AU - Ávila Carneiro T.
AU - Augusto Gonçalves do Canto J.
PY - 2011
SP - 509
EP - 512
DO - 10.5220/0003675305090512