Large unbalanced credit scoring using Lasso-logistic regression ensemble.
Large unbalanced credit scoring using Lasso-logistic regression ensemble.
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DOI：
10.1371/journal.pone.0117844
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zhou L
中科院分区：
文献类型：
--
作者：
Wang H;Xu Q;Zhou L
近年来，针对信用评分问题，人们提出了不同基分类器的集成学习方法。然而，由于种种原因，使用Logistic回归作为基本分类器的研究还很少。本文考虑了在大量不平衡数据的情况下，利用正则化Logistic回归作为基础分类器进行集成学习来处理信用评分问题的可行性。在本研究中，首先通过聚类和打包算法对数据进行均衡和多样化。然后应用Lasso-Logistic回归学习集成对信用风险进行评估。实验结果表明，该算法在AUC和F度量方面均优于决策树、Lasso-Logistic回归和随机森林等常用的信用评分模型。我们还为所提出的模型提供了两个重要度量，以识别数据中的重要变量。
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.
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