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Akaike, H. (1973) Information Theory and Extension of the Maximum Likelihood Principle. In: Parzen, E., Tanabe, K. and Kitagawa, G., Eds., Selected Papers of Hirotugu Akaike, Springer, New York, 267-281.

has been cited by the following article:

  • TITLE: Logistic and SVM Credit Score Models Based on Lasso Variable Selection

    AUTHORS: Qingqing Li

    KEYWORDS: Credit Evaluation, Logistic Algorithm, SVM Algorithm, Lasso Variable Selection

    JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.7 No.5, May 27, 2019

    ABSTRACT: There are many factors influencing personal credit. We introduce Lasso technique to personal credit evaluation, and establish Lasso-logistic, Lasso-SVM and Group lasso-logistic models respectively. Variable selection and parameter estimation are also conducted simultaneously. Based on the personal credit data set from a certain lending platform, it can be concluded through experiments that compared with the full-variable Logistic model and the stepwise Logistic model, the variable selection ability of Group lasso-logistic model was the strongest, followed by Lasso-logistic and Lasso-SVM respectively. All three models based on Lasso variable selection have better filtering capability than stepwise selection. In the meantime, the Group lasso-logistic model can eliminate or retain relevant virtual variables as a group to facilitate model interpretation. In terms of prediction accuracy, Lasso-SVM had the highest prediction accuracy for default users in the training set, while in the test set, Group lasso-logistic had the best classification accuracy for default users. Whether in the training set or in the test set, the Lasso-logistic model has the best classification accuracy for non-default users. The model based on Lasso variable selection can also better screen out the key factors influencing personal credit risk.