FORECASTING OF CREDIT CLASSES WITH THE SELF-ORGANIZING MAPS

To determine the credit classes statistical and artificial intelligence methods have been used often recently. Particularly the artificial neural networks there have been often applied, one of them is a self-organizing map (SOM). SOM is a two-dimensional map of the credit units that is generated by similar characteristics (attributes) of the process. However this process is not specified by network outputs. If the credit units of one class dominate in the clusters it is valuable such SOMs to employ to forecast the new credit classes. In this paper we investigate the capabilities of SOM in forecasting of credit classes. We present the results of our investigations and show that SOM may distinctly reduce misclassification errors. On the other hand, we demonstrate the possibility of SOM to identify how dataset is liable for the valuable map generation.

[1]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[2]  J. Galindo,et al.  Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications , 2000 .

[3]  Kanter,et al.  Eigenvalues of covariance matrices: Application to neural-network learning. , 1991, Physical review letters.

[4]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[5]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[6]  ChenHsinchun,et al.  Credit rating analysis with support vector machines and neural networks , 2004 .

[7]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[8]  Selwyn Piramuthu,et al.  Financial credit-risk evaluation with neural and neurofuzzy systems , 1999, Eur. J. Oper. Res..

[9]  Kaisa Sere,et al.  Analyzing financial performance with self-organizing maps , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[10]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[11]  G. Deboeck.  SELF-ORGANIZING MAPS FACILITATE KNOWLEDGE DISCOVERY IN FINANCE , 1998 .

[12]  Kaisa Sere,et al.  Competitive financial benchmarking using self-organizing maps , 1997 .

[13]  Kimmo Kiviluoto,et al.  Predicting bankruptcies with the self-organizing map , 1998, Neurocomputing.

[14]  Guido Deboeck.  Financial Applications of Self-Organizing Maps , 1998 .

[15]  Mu-Chun Su,et al.  An efficient initialization scheme for the self-organizing feature map algorithm , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).