Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
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Neural Processing Letters
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The Journal of Machine Learning Research
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Pattern Recognition
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
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Feature selection in bankruptcy prediction
Knowledge-Based Systems
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
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Knowledge-Based Systems
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Computers & Mathematics with Applications
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Knowledge-Based Systems
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
International Journal of Hybrid Intelligent Systems
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Corporate bankruptcy prediction is very important for creditors and investors. Most literature improves performance of prediction models by developing and optimizing the quantitative methods. This paper investigates the effect of sampling methods on the performance of quantitative bankruptcy prediction models on real highly imbalanced dataset. Seven sampling methods and five quantitative models are tested on two real highly imbalanced datasets. A comparison of model performance tested on random paired sample set and real imbalanced sample set is also conducted. The experimental results suggest that the proper sampling method in developing prediction models is mainly dependent on the number of bankruptcies in the training sample set.