Article2015

Feature Selection in Credit Scoring Model for Credit Card Applicants in XYZ Bank: A Comparative Study

Mediana Aryuni · Evaristus Didik Madyatmadja

12 citations0/0 files storedInternational Journal of Multimedia and Ubiquitous Engineering

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Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

The performance of credit scoring models is determined by the used features. The relevant features for credit scoring usually are determined unsystematic and dominate by arbitrary trial. This paper presents a comparative study of four feature selection methods, which use data mining approach in reducing the feature space. The final results show that among the four feature selection methods, the Gini Index and Information Gain algorithms perform better than others with the classification accuracy of 75.46% and 75.44% respectively.

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References

2008Support vector machines for credit scoring and discovery of significant features 2004An integrated data mining and behavioral scoring model for analyzing bank customers 2009Combination of feature selection approaches with SVM in credit scoring 2005Data mining feature selection for credit scoring models 2005Filter- versus wrapper-based feature selection for credit scoring 2007Credit risk analysis using a hybrid data mining model 2013Weight-Selected Attribute Bagging for Credit Scoring

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