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首页> 外文期刊>Progress in Artificial Intelligence >Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market
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Cost-sensitive ensemble methods for bankruptcy prediction in a highly imbalanced data distribution: a real case from the Spanish market

机译：在高度不平衡数据分布中破产预测的成本敏感的集成方法：来自西班牙市场的真正案例

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摘要

Bankruptcy is an issue of interest in the business world since decades. It is a crucial endeavor for survival to predict this phenomenon in periods of economic turmoil and recession. In fact, bankruptcy modeling is challenging due to the complexity of contributing factors and the highly imbalanced distribution of available data sets. This work aims at improving the prediction power of bankruptcy modeling, by applying cost-sensitive ensemble methods on a real-world Spanish bankruptcy data set to generate prediction models. The performance of the prediction models is highly competitive in comparison with the related research in the field. Cost-sensitive random forests over-performed other approaches in predicting bankruptcy, achieving a geometric mean of 90.7%, 0.094 and 0.088 type I & type II errors, respectively.
机译：几十年来，破产一直是商界关注的问题。在经济动荡和衰退时期预测这种现象对生存至关重要。事实上，由于成因的复杂性和可用数据集的高度不平衡分布，破产建模具有挑战性。这项工作的目的是通过在真实的西班牙破产数据集上应用成本敏感的集成方法来生成预测模型，从而提高破产建模的预测能力。与该领域的相关研究相比，预测模型的性能具有很强的竞争力。成本敏感随机森林在预测破产方面比其他方法表现更好，其几何平均值分别为90.7%、0.094和0.088 I型和II型误差。

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