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Authors: Maria Luiza F. Velloso 1 ; Nival N. Almeida 1 ; Thales Ávila Carneiro 1 and José Augusto Gonçalves do Canto 2

Affiliations: 1 Rio de Janeiro State University, Brazil ; 2 Institute of Political Science and Economics and Candido Mendes University, Brazil

Keyword(s): Supervised clustering, Fuzzy modelling, Interpretability.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Fuzzy Systems ; Fuzzy Systems Design, Modeling and Control ; Pattern Recognition: Fuzzy Clustering and Classifiers ; Soft Computing

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 medi um 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. (More)

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Paper citation in several formats:
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 (IJCCI 2011) - FCTA; ISBN 978-989-8425-83-6, SciTePress, pages 509-512. DOI: 10.5220/0003675305090512

@conference{fcta11,
author={Maria Luiza {F. Velloso}. and Nival {N. Almeida}. and Thales {Ávila Carneiro}. and José {Augusto Gon\c{C}alves 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 (IJCCI 2011) - FCTA},
year={2011},
pages={509-512},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003675305090512},
isbn={978-989-8425-83-6},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications (IJCCI 2011) - FCTA
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
PB - SciTePress