A Novel Imbalanced Data Classification Approach Based on Logistic Regression and Fisher Discriminant

作者：Shi, Baofeng^*; Wang, Jing; Qi, Junyan; Cheng, Yanqiu

来源：Mathematical Problems in Engineering, 2015, 2015: 945359.

DOI：10.1155/2015/945359

• CREDIT
• PREDICTION
• MODEL

We introduce an imbalanced data classification approach based on logistic regression significant discriminant and Fisher discriminant. First of all, a key indicators extraction model based on logistic regression significant discriminant and correlation analysis is derived to extract features for customer classification. Secondly, on the basis of the linear weighted utilizing Fisher discriminant, a customer scoring model is established. And then, a customer rating model where the customer number of all ratings follows normal distribution is constructed. The performance of the proposed model and the classical SVM classification method are evaluated in terms of their ability to correctly classify consumers as default customer or nondefault customer. Empirical results using the data of 2157 customers in financial engineering suggest that the proposed approach better performance than the SVM model in dealing with imbalanced data classification. Moreover, our approach contributes to locating the qualified customers for the banks and the bond investors.

• 出版日期2015
• 单位东北财经大学; 西北农林科技大学; 河南理工大学