Abstract
This study tests the sensitivity of input feature space selection on credit rating using four classifiers as backpropagation(BP), Kohonen self-organizing feature map, discriminant analysis(DA), and logistic regression. The results of the study are that at individual methods applied, BP network outperforms two statistical counterparts while Kohonen network shows the least accuracy among the models. The results also show that the selection of the feature spaces to the accuracy outcome may not be very sensitive when we test the four methodologies altogether at aggregate level.
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Park, J., Lee, K., Kim, J. (2007). Effectiveness of Feature Space Selection on Credit Engineering on Multi-group Classification Cases. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_93
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DOI: https://doi.org/10.1007/978-3-540-71618-1_93
Publisher Name: Springer, Berlin, Heidelberg
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