A Hybrid Model of AdaBoost and Back-Propagation Neural Network for Credit Scoring

被引：1

作者：

Shen, Feng ^{[1
]}

Zhao, Xingchao ^{[1
]}

Lan, Dao ^{[2
]}

Ou, Limei ^{[2
]}

机构：

[1] Southwestern Univ Finance & Econ, Sch Finance, Chengdu 611130, Sichuan, Peoples R China

[2] Southwestern Univ Finance & Econ, Chengdu 611130, Sichuan, Peoples R China

来源：

PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT | 2018年

关键词：

Credit scoring; AdaBoost model; Back-propagation neural network; SUPPORT VECTOR MACHINES; PREDICTION;

D O I：

10.1007/978-3-319-59280-0_6

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Owing to the development of internet finance in China, credit scoring is growing into one of the most important issues in the field of financial risk management. Quantitative credit scoring models are widely used tools for credit risk assessment in financial institutions. In this study, an AdaBoost algorithm model based on back-propagation neural network for credit scoring with high accuracy and efficiency is proposed. We first illustrate the basic concepts of back-propagation neural network and AdaBoost algorithm and propose a hybrid model of AdaBoost and back-propagation neural network, then two real-world credit data sets are selected to demonstrate the effectiveness and feasibility of the proposed model. The results show that the proposed model can get higher accuracy compared to other classifiers listed in this study.

引用

页码：78 / 90

页数：13

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