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• W1980931745 startingPage "e0117844" @default.
• W1980931745 abstract "Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data." @default.
• W1980931745 created "2016-06-24" @default.
• W1980931745 creator A5035053697 @default.
• W1980931745 creator A5050510965 @default.
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• W1980931745 date "2015-02-23" @default.
• W1980931745 modified "2023-10-05" @default.
• W1980931745 title "Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble" @default.
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• W1980931745 doi "https://doi.org/10.1371/journal.pone.0117844" @default.
• W1980931745 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4338292" @default.
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• W1980931745 hasPublicationYear "2015" @default.
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