Modeling the efficiency of top Arab banks: A DEA-neural network approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Profiling blood donors in Egypt: A neural network analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the ecological footprint of nations
Computational Statistics & Data Analysis
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait
Expert Systems with Applications: An International Journal
A neuro-computational intelligence analysis of the global consumer software piracy rates
Expert Systems with Applications: An International Journal
Decision tree-based technology credit scoring for start-up firms: Korean case
Expert Systems with Applications: An International Journal
Elucidating clinical context of lymphopenia by nonlinear modelling
Expert Systems with Applications: An International Journal
Assessment of financial risk prediction models with multi-criteria decision making methods
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Previous research on credit scoring that used statistical and intelligent methods was mostly focused on commercial and consumer lending. The main purpose of this paper is to extract important features for credit scoring in small-business lending on a dataset with specific transitional economic conditions using a relatively small dataset. To do this, we compare the accuracy of the best models extracted by different methodologies, such as logistic regression, neural networks (NNs), and CART decision trees. Four different NN algorithms are tested, including backpropagation, radial basis function network, probabilistic and learning vector quantization, by using the forward nonlinear variable selection strategy. Although the test of differences in proportion and McNemar's test do not show a statistically significant difference in the models tested, the probabilistic NN model produces the highest hit rate and the lowest type I error. According to the measures of association, the best NN model also shows the highest degree of association with the data, and it yields the lowest total relative cost of misclassification for all scenarios examined. The best model extracts a set of important features for small-business credit scoring for the observed sample, emphasizing credit programme characteristics, as well as entrepreneur's personal and business characteristics as the most important ones. Copyright © 2005 John Wiley & Sons, Ltd.