Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Decision Support Systems - Special issue: Data mining for financial decision making
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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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
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
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Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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A loan default discrimination model using cost-sensitive support vector machine improved by PSO
Information Technology and Management
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The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones, respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with those of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a promising alternative to the existing methods for bankruptcy prediction.