Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Hybrid Classifiers for Financial Multicriteria Decision Making: TheCase of Bankruptcy Prediction
Computational Economics
Credit Scoring and Its Applications
Credit Scoring and Its Applications
Soft computing system for bank performance prediction
Applied Soft Computing
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
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We propose the particle swarm optimization (PSO) trained auto associative neural network (AANN) as a single class classifier (PSOAANN). The proposed architecture consists of three layers namely input layer, hidden layer and output layer unlike that of the traditional AANN. The efficacy of the proposed single class classifier is evaluated on bankruptcy prediction datasets namely Spanish banks, Turkish banks, US banks and UK banks; UK credit dataset and the benchmark WBC dataset. PSOAANN achieved better results when compared to Modified Great Deluge Algorithm trained auto associative neural network (MGDAAANN) [1]. It is concluded that PSOAANN as a single class classifier can be used as an effective tool in classifying datasets, where the class of interest (usually the positive class) is either totally missing or disproportionately present in the training data, which is the case in many real life problems for e.g. financial fraud detection.