论文标题
信用评分的动态合奏学习：比较研究
Dynamic Ensemble Learning for Credit Scoring: A Comparative Study
论文作者
论文摘要
自动信用评分评估了贷款申请人违约的可能性，在点对点贷款平台中起着至关重要的作用，以降低贷方的风险。尽管已经证明动态选择技术对于分类任务有效，但尚未确定这些信用评分技术的性能。这项研究试图系统地对集合学习模型进行基于不同的动态选择方法，以准确估计大型且高维的现实生活中的信用评分数据集上的信用评分任务。这项研究的结果表明，动态选择技术能够提高集成模型的性能，尤其是在不平衡的训练环境中。
Automatic credit scoring, which assesses the probability of default by loan applicants, plays a vital role in peer-to-peer lending platforms to reduce the risk of lenders. Although it has been demonstrated that dynamic selection techniques are effective for classification tasks, the performance of these techniques for credit scoring has not yet been determined. This study attempts to benchmark different dynamic selection approaches systematically for ensemble learning models to accurately estimate the credit scoring task on a large and high-dimensional real-life credit scoring data set. The results of this study indicate that dynamic selection techniques are able to boost the performance of ensemble models, especially in imbalanced training environments.
