A data-distribution based imbalanced data classification method for credit scoring using neural networks

被引：0

作者：

Zhang, Dailing ^{[1
]}

Xu, Wei ^{[1
]}

机构：

[1] Renmin Univ China, Sch Informat, Beijing, Peoples R China

来源：

2013 SIXTH INTERNATIONAL CONFERENCE ON BUSINESS INTELLIGENCE AND FINANCIAL ENGINEERING (BIFE) | 2014年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

credit scroing; imbalanced data; data-distribution; classification;

D O I：

10.1109/BIFE.2013.116

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Credit scoring is always a hot topic for the researchers because of its profitability. In this paper, we proposed a novel data-distribution based imbalanced data classification method to construct the credit scoring model using BP neural networks. The method distinguished itself by focusing on the distribution of the data and artificially changes the probabilities of the sampling for the purpose of centralizing the edge samples. The German Credit Dataset is applied for verifying the effectiveness of the method, and the experiment results show that the classifiers constructed by the proposed method performs better for the imbalanced credit data classification.

引用

页码：557 / 561

页数：5

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