Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods
Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods
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DOI：
10.1016/j.knosys.2012.12.007
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发表时间：
2013-03
期刊：
影响因子：
--
通讯作者：
Ligang Zhou
中科院分区：
文献类型：
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作者：
Ligang Zhou
企业破产预测对于债权人和投资者来说都是非常重要的。大多数文献通过发展和优化定量方法来提高预测模型的性能。本文研究了抽样方法对真实的高度不平衡数据集上定量破产预测模型性能的影响。在两个真实的高度不平衡的数据集上测试了七种抽样方法和五种定量模型。在随机配对样本集和真实的不平衡样本集上对模型性能进行了比较。实验结果表明，在建立预测模型时，适当的抽样方法主要取决于训练样本集中破产的数量。
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.