Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
Journal of Global Optimization
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
On the Kernel Widths in Radial-Basis Function Networks
Neural Processing Letters
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Soft computing system for bank performance prediction
Applied Soft Computing
Fast learning in networks of locally-tuned processing units
Neural Computation
Software development cost estimation using wavelet neural networks
Journal of Systems and Software
Expert Systems with Applications: An International Journal
Financial distress prediction by a radial basis function network with logit analysis learning
Computers & Mathematics with Applications
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
The study of grid task scheduling based on AFSA algorithm
International Journal of Computer Applications in Technology
Rule extraction from DEWNN to solve classification and regression problems
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Engineering Applications of Artificial Intelligence
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In this paper, we propose differential evolution (DE) to train the supervised part of the radial basis function (RBF) network in the soft computing paradigm. Here the unsupervised part of the RBF is taken care of by K-means clustering. The new network is named as differential evolution trained radial basis function (DERBF) network. The efficacy of DERBF is tested on bank bankruptcy datasets viz. Spanish banks, Turkish banks, US banks and UK banks as well as benchmark datasets such as iris, wine and Wisconsin breast cancer. The performance of DERBF is compared with that of differential evolution trained wavelet neural networks (DEWNN) (Chauhan et al., 2009), threshold accepting trained wavelet neural network (TAWNN) (Vinaykumar et al., 2008) and wavelet neural network with respect to the criterion area under receiver operating characteristic curve. The results showed that DERBF is very good at generalisation in the ten-fold cross validation for all datasets.