基于树的异构级联集成信用评分模型

IF 6.9 2区 经济学 Q1 ECONOMICS International Journal of Forecasting Pub Date : 2023-10-01 DOI:10.1016/j.ijforecast.2022.07.007
Wanan Liu , Hong Fan , Meng Xia
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引用次数: 4

摘要

信用评分是银行和贷款公司防范商业风险的重要工具,为个人信用建设提供了良好的条件。集成算法在信用评分的改进方面取得了令人满意的进展。在本研究中,为了应对大规模信用评分的挑战,我们提出了一种异构深度森林模型(Heter-DF),该模型基于基础学习者选择、鼓励基础学习者多样性和集成策略等方面的考虑,用于信用评分。Heter-DF被设计为一个可扩展的级联框架,可以随着信用数据集的规模增加其复杂性。此外,Heter-DF的每一层由多个基于异构树的集成基学习器构建,避免了集成框架的同质预测。此外,引入加权投票机制来突出重要信息并抑制无关特征,使Heter-DF成为一个鲁棒的信用评分模型。在4个信用评分数据集和6个评价指标上的实验结果表明,级联框架是树基学习器集成的良好选择。通过对均匀集成和非均匀集成的比较,进一步证明了Heter-DF的有效性。在不同训练集上的实验表明,Heter-DF是一个可扩展的框架,既能处理大规模的信用评分,又能满足需要小规模信用评分的条件。最后,基于树型结构良好的可解释性,对Heter-DF的全局解释进行了初步探讨。
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Tree-based heterogeneous cascade ensemble model for credit scoring

Credit scoring is an important tool to guard against commercial risks for banks and lending companies and provides good conditions for the construction of individual personal credit. Ensemble algorithms have shown appealing progress for the improvement of credit scoring. In this study, to meet the challenge of large-scale credit scoring, we propose a heterogeneous deep forest model (Heter-DF), which is established based on considerations ranging from base learner selection, encouragement of the diversity of base learners, and ensemble strategies, for credit scoring. Heter-DF is designed as a scalable cascading framework that can increase its complexity with the scale of the credit dataset. Moreover, each level of Heter-DF is built by multiple heterogeneous tree-based ensembled base learners, avoiding the homogeneous prediction of the ensemble framework. In addition, a weighted voting mechanism is introduced to highlight important information and suppress irrelevant features, making Heter-DF a robust model for credit scoring. Experimental results on four credit scoring datasets and six evaluation metrics show that the cascading framework a good choice for the ensemble of tree-based base learners. A comparison among homogeneous ensembles and heterogeneous ensembles further demonstrates the effectiveness of Heter-DF. Experiments on different training sets indicate that Heter-DF is a scalable framework which not only deals with large-scale credit scoring but also satisfies the condition where small-scale credit scoring is desirable. Finally, based on the good interpretability of a tree-based structure, the global interpretation of Heter-DF is preliminarily explored.

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来源期刊
CiteScore
17.10
自引率
11.40%
发文量
189
审稿时长
77 days
期刊介绍: The International Journal of Forecasting is a leading journal in its field that publishes high quality refereed papers. It aims to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research, and improving the practice of forecasting. It welcomes various points of view and encourages debate to find solutions to field-related problems. The journal is the official publication of the International Institute of Forecasters (IIF) and is indexed in Sociological Abstracts, Journal of Economic Literature, Statistical Theory and Method Abstracts, INSPEC, Current Contents, UMI Data Courier, RePEc, Academic Journal Guide, CIS, IAOR, and Social Sciences Citation Index.
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