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Erschienen in:
Advancement of Computing Methodology, Data Analysis, Enterprise Operation Management and Decision Support System Based on the Eleventh ICMSEM Proceedings Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

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

verfasst von : Feng Shen, Xingchao Zhao, Dao Lan, Limei Ou

Erschienen in: Proceedings of the Eleventh International Conference on Management Science and Engineering Management

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel An Order-Based GA for Robot-Based Assembly Line Balancing Problem
Nächstes Kapitel Effects of Urban and Rural Residents’ Behavior Differences in Sports and Leisure Activity: Application of the Theory of Planned Behavior and Structural Equation Modeling
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Metadaten
Titel
A Hybrid Model of AdaBoost and Back-Propagation Neural Network for Credit Scoring
verfasst von
Feng Shen
Xingchao Zhao
Dao Lan
Limei Ou
Copyright-Jahr
2018
Buch
Proceedings of the Eleventh International Conference on Management Science and Engineering Management

Print ISBN: 978-3-319-59279-4

Electronic ISBN: 978-3-319-59280-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-59280-0_6

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