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Yeh, I.-C. and Lien, C.-H. (2009) The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients. Expert Systems with Applications, 36, 2473-2480.
https://doi.org/10.1016/j.eswa.2007.12.020

has been cited by the following article:

• TITLE: Comparison of Several Data Mining Methods in Credit Card Default Prediction

  AUTHORS: Shenghui Yang, Haomin Zhang

  KEYWORDS: LightGBM, Xgboost, AUC, F1-Score, Data Mining

  JOURNAL NAME: Intelligent Information Management, Vol.10 No.5, September 25, 2018

  ABSTRACT: LightGBM is an open-source, distributed and high-performance GB framework built by Microsoft company. LightGBM has some advantages such as fast learning speed, high parallelism efficiency and high-volume data, and so on. Based on the open data set of credit card in Taiwan region, five data mining methods, Logistic regression, SVM, neural network, Xgboost and LightGBM, are compared in this paper. The results show that the AUC, F1-Score and the predictive correct ratio of LightGBM are the best, and that of Xgboost is second. It indicates that LightGBM or Xgboost has a good performance in the prediction of categorical response variables and has a good application value in the big data era.