Instance sampling in credit scoring: An empirical study of sample size and balancing
Instance sampling in credit scoring: An empirical study of sample size and balancing
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DOI：
10.1016/j.ijforecast.2011.07.006
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发表时间：
2012-01-01
影响因子：
7.9
通讯作者：
Finlay, Steven
中科院分区：
文献类型：
--
作者：
Crone, Sven F.;Finlay, Steven
到目前为止，对信贷申请人进行抽样的最佳做法主要是根据专家意见建立的，专家意见通常建议商品和坏品各1500个实例的小样本就足够了，并且观察到的严重偏差的数据集应该通过对大多数类别进行欠采样来平衡。因此，样本大小和样本平衡的主题没有受到信用评分的正式研究，或在不同的数据条件和不同效率的算法的实证评估。本文描述了一个实证研究的实例抽样在预测消费者的还款行为，评估的相对准确性的逻辑回归，判别分析，决策树和神经网络的两个数据集上的20个样本的大小不断增加和29个重新平衡的样本分布逐渐下和过采样的商品和坏分别创建。本文对信用评分数据集的模型构建做出了实际贡献，并提供了证据表明，使用比信用评分实践中推荐的样本更大的样本可以显着提高算法的准确性。(C)2011年，国际预报员协会。Elsevier B.V.出版，保留所有权利。
To date, best practice in sampling credit applicants has been established based largely on expert opinion, which generally recommends that small samples of 1500 instances each of both goods and bads are sufficient, and that the heavily biased datasets observed should be balanced by undersampling the majority class. Consequently, the topics of sample sizes and sample balance have not been subject to either formal study in credit scoring, or empirical evaluations across different data conditions and algorithms of varying efficiency. This paper describes an empirical study of instance sampling in predicting consumer repayment behaviour, evaluating the relative accuracies of logistic regression, discriminant analysis, decision trees and neural networks on two datasets across 20 samples of increasing size and 29 rebalanced sample distributions created by gradually under- and over-sampling the goods and bads respectively. The paper makes a practical contribution to model building on credit scoring datasets, and provides evidence that using samples larger than those recommended in credit scoring practice provides a significant increase in accuracy across algorithms. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.