Dynamic credit scoring using B & B with incremental-SVM-ensemble
Dynamic credit scoring using B & B with incremental-SVM-ensemble
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DOI：
10.1108/k-02-2014-0036
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发表时间：
2015-06
期刊：
影响因子：
2.5
通讯作者：
Jie Sun;Hui Li;P. Chang;Qinghua Huang
中科院分区：
文献类型：
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作者：
Jie Sun;Hui Li;P. Chang;Qinghua Huang
– 以往的信用评分研究主要集中在一定时期内面板样本数据集上的静态建模，而对动态增量建模关注不够。本文的目的是解决分支定界算法与增量支持向量机（SVM）集成的集成问题，以进行信用评分的动态建模。 , – 这种新模型在基于袋装支持向量机的动态集成建模过程中将旧数据的支持向量与企业增量财务数据混合在一起。在增量阶段，多个基础SVM模型根据信用评分的新更新信息进行动态调整。这些更新的基本模型进一步组合以生成动态信用评分。在实证实验中，将新方法与传统的非增量SVM集成信用评分模型进行了比较。 , – 结果表明，新模型能够根据企业增量信息持续动态调整信用评分，从而产生比传统模型更好的评价能力。 , – 这项研究开创了使用增量 SVM 集成进行信用评分动态建模的研究。随着时间的推移，新的增量样本将与旧样本的支持向量相结合，构建SVM集成信用评分模型。增量模型会不断自我调整以保持良好的评估性能。
– Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring. , – This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring. , – The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model. , – This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.