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

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ID: 104890
2015
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Abstract
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.
Reference Key
shi2015amathematical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shi, Baofeng;Wang, Jing;Qi, Junyan;Cheng, Yanqiu;
Journal mathematical problems in engineering
Year 2015
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2015/945359
Keywords
chemistry Biology (General) Technology (General) Engineering (General). Civil engineering (General) Technology Science (General) Science Medical technology physics telecommunication mathematics electronic computers. computer science

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