Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification

2017 ◽  
Vol 29 (9) ◽  
pp. 1806-1819 ◽  
Author(s):  
Miho Ohsaki ◽  
Peng Wang ◽  
Kenji Matsuda ◽  
Shigeru Katagiri ◽  
Hideyuki Watanabe ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Confusion Matrix ◽  
Imbalanced Data ◽  
Data Classification ◽  
Kernel Logistic Regression ◽  
Imbalanced Data Classification

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

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Baofeng Shi ◽  
Jing Wang ◽  
Junyan Qi ◽  
Yanqiu Cheng
Keyword(s):  
Logistic Regression ◽  
Imbalanced Data ◽  
Data Classification ◽  
Classification Approach ◽  
Fisher Discriminant ◽  
Proposed Model ◽  
Imbalanced Data Classification ◽  
Customer Classification ◽  
Key Indicators ◽  
Rating 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.

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