Journal of Computational Physics
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
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ITI '96 Selected papers from the 18th international conference on Information technology interfaces
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IEEE Transactions on Neural Networks
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Failure prediction of dotcom companies using neural network-genetic programming hybrids
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On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction
Expert Systems with Applications: An International Journal
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International Journal of Bio-Inspired Computation
CROS: A Contingency Response multi-agent system for Oil Spills situations
Applied Soft Computing
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel neural network method for shortest path tree computation
Applied Soft Computing
Enhanced fuzzy-filtered neural networks for material fatigue prognosis
Applied Soft Computing
International Journal of Intelligent Systems in Accounting and Finance Management
International Journal of Information Systems and Social Change
International Journal of Hybrid Intelligent Systems
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This paper proposes an application of new principal component neural network (PCNN) architecture to bankruptcy prediction problem in commercial banks. Further, a new feature subset selection (FSS) algorithm is proposed. In this architecture, the hidden layer is completely replaced by what is referred to as a 'principal component layer'. This layer consists of a few selected principal components that perform the function of hidden nodes. Moreover, this study proposes an algorithm based on the threshold accepting (TA) meta-heuristic to train the PCNN. The architecture reduces the number of weights by a great number as there are no formal connections between the input layer and the principal component layer. The efficacy of the algorithm is tested on the Spanish banks dataset and Turkish banks dataset. The results showed high generalization power of PCNN in the 10-fold cross-validation and also the feature subsets selected in each of the examples showed high discriminating power. PCNN is also compared with PCA-TANN and PCA-BPNN, which have PCA as the preprocessor and have one hidden layer each. Further comparisons are also made with TANN and BPNN. All these classifiers are compared with respect to the AUC (area under the receiver operating characteristic (ROC) curve) criterion. ROC curve is drawn for each classifier with sensitivity on the X-axis and one-specificity on the Y-axis. Based on the experiments conducted, it is inferred that the proposed PCNN hybrids outperformed other classifiers in terms of AUC. It is also observed that the proposed feature subset selection algorithm is very stable and powerful.