Application and comparison of neural network, C5.0, and classification and regression trees algorithms in the credit risk evaluation problem (case study: a standard German credit dataset)

Mahdi Massahi Khoraskani; Fahimeh Kheradmand; Alireza Arshadi Khamseh

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Application and comparison of neural network, C5.0, and classification and regression trees algorithms in the credit risk evaluation problem (case study: a standard German credit dataset)

机译：神经网络，C5.0以及分类和回归树算法在信用风险评估问题中的应用和比较（案例研究：标准的德国信用数据集）

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摘要

Due to the reducing global economic stability, the demand of banks for predicting their customer's credit risk has significantly increased and has become more critical, still challenging than ever. This paper addresses the problem of credit risk evaluation of bank's customers utilising data mining tools. Three classification techniques include: neural network, C5.0, and classification and regression trees (CART) algorithms. In order to evaluate the performance of the classification techniques, an innovative two-stage evaluation process is proposed. Firstly, the optimal status of algorithms is found by tuning its parameters. Secondly, these tuned algorithms are ranked by the analytical hierarchy process (AHP) method while four criteria of overall accuracy, precision, sensitivity, and specificity are considered. As a case study, a standard German credit dataset are used to validate the performance of the proposed algorithms. It is illustrated that the neural network algorithm is the superior algorithm to evaluate bank customers' credit risk.

机译：由于全球经济稳定程度的下降，银行预测其客户信用风险的需求已大大增加，并且变得越来越关键，仍然比以往更具挑战性。本文解决了利用数据挖掘工具对银行客户进行信用风险评估的问题。三种分类技术包括：神经网络，C5.0以及分类和回归树（CART）算法。为了评估分类技术的性能，提出了一种创新的两阶段评估程序。首先，通过调整其参数来找到算法的最佳状态。其次，这些调整后的算法通过层次分析法（AHP）进行排名，同时考虑了总体准确性，准确性，敏感性和特异性四个标准。作为案例研究，使用标准的德国信用数据集来验证所提出算法的性能。说明了神经网络算法是评价银行客户信用风险的优良算法。

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来源
《International Journal of Knowledge Engineering and Data Mining》 |2017年第4期|259-276|共18页
作者
Mahdi Massahi Khoraskani; Fahimeh Kheradmand; Alireza Arshadi Khamseh;
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作者单位

Department of Industrial Engineering and Management Sciences, Amirkabir University of Technology, Tehran, Iran;

Department of Industrial Engineering and Management Sciences, Amirkabir University of Technology, Tehran, Iran;

Department of Industrial Engineering, College of Engineering, University of Kharazmi, Tehran, Iran;

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正文语种 eng
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关键词
credit risk evaluation; data mining; classification; neural networks; C50; classification and regression trees; CARTs; analytical hierarchy process; AHP;

机译：信用风险评估;数据挖掘;分类;神经网络;C50;分类回归树;CARTs;层次分析法;层次分析法;

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Application and comparison of neural network, C5.0, and classification and regression trees algorithms in the credit risk evaluation problem (case study: a standard German credit dataset)

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