Building the ensembles of credit scoring models

Halyna Velykoivanenko, S. Savina, D. Kolechko, Vladyslav Ben'
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引用次数: 1

Abstract

The article is devoted to solving the actual problem of increasing the efficiency of assessing the credit risks of individual borrowers by finding the optimal combination of the results of calculations of specific scoring models. The principles of the formation of an ensemble of models are given and the existing approaches to the construction of ensemble structures are analyzed. In the process of experimental research has been applied one of the modifications of the boosting algorithm and implemented the author's algorithm for constructing an ensemble of models based on the specialization of experts. The radial-basis function neural networks were used as specific expert models. As a result of a comparative analysis of the efficiency of the used ensemble technologies it was confirmed that the algorithm for constructing an ensemble based on the specialization of experts proposed by the authors is the most adapted for the task of assessing credit risk.
构建信用评分模型的集合
本文致力于通过寻找具体评分模型计算结果的最优组合,来解决提高个人借款人信用风险评估效率的实际问题。给出了模型集合的形成原理,分析了现有的模型集合结构的构造方法。在实验研究过程中,应用了对增强算法的一种改进,实现了基于专家专业化的模型集成构建算法。采用径向基函数神经网络作为专家模型。通过对常用集成技术效率的比较分析,证实了本文提出的基于专家专业化的集成构建算法最适合于信用风险评估任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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