新型光滑正则半监督SVM方法及其在信用评级中的应用

计算机科学 ›› 2013, Vol. 40 ›› Issue (10): 239-242.

新型光滑正则半监督SVM方法及其在信用评级中的应用

薛飞,鲁利民,王磊

西南财经大学经济信息工程学院成都610072;西南财经大学西部商学院成都610072;西南财经大学经济信息工程学院成都610072

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受教育部人文社会科学研究一般项目(10YJCZH153)资助

Novel Smooth Regularization Based Semi-supervised SVM Approach and its Application in Credit Evaluation

XUE Fei,LU Li-min and WANG Lei

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 提出了一种基于光滑正则的半监督支持向量机方法,并将其用于建立中小信用评级模型。它从少量标签样本和大量无标签样本中构造反映数据流形结构的光滑正则项,并结合到支持向量机的最大间隔分类器的学习过程。然后,提出一种渐进式方法来迭代获得“半标签”样本,稳健地提升支持向量机的泛化性能。在真实数据集上的实验结果表明,新方法获得的测试精度显著优于多种现有方法,非常适用于中小企业的信用评级任务。

关键词: 支持向量机,信用评级,半监督学习,光滑正则

Abstract: This paper proposed a novel smooth regularization based semi-supervised support vector machine approach,and applied it to set up credit evaluation model of small-and-medium enterprises．It computes a manifold-related smooth regularization term on both few labeled samples and plenty of unlabeled samples,which is combined into the learning process of maximal margin classifiers．Then,it adopts a progressive method to acquire semi-labeled samples iteratively so that the generalization performance of support vector machine can be improved gradually．Experiments on reality dataset show that the testing accuracy of proposed approach outperforms several popular ones,and is very suitable for evaluating credit grades of small-and-medium enterprises．

Key words: Support vector machines,Credit evaluation,Semi-supervised learning,Smooth regularization

薛飞,鲁利民,王磊. 新型光滑正则半监督SVM方法及其在信用评级中的应用[J]. 计算机科学, 2013, 40(10): 239-242. https://doi.org/

XUE Fei,LU Li-min and WANG Lei. Novel Smooth Regularization Based Semi-supervised SVM Approach and its Application in Credit Evaluation[J]. Computer Science, 2013, 40(10): 239-242. https://doi.org/

参考文献

[1] 徐志春．我国商业银行中小企业信用风险管理研究[D]．武汉:华中科技大学,2012
[2] Desai V S,Crook J N,Jr G A．A comparison of neural networks and linear scoring models in the credit union environment[J]．European Journal of Operational Research,1996,95(1):24-39
[3] 李萌,陈柳钦.基于BP神经网络的商业银行信用风险识别实证分析[J]．南京社会科学,2007(1):18-29
[4] 张学工．统计学习理论的本质[M]．北京:清华大学出版社,2000
[5] 庞素琳.信用评价与股市预测模型研究及应用[M]．北京:科学出版社,2005
[6] Gestel V,Baesens B,Garcia J,et al．A support vector machine approach to credit scoring[J]．Bank en Financiewezen,2003(2):73-82
[7] Martensa D,Baesensb B,Gestelc V,et al．Comprehensible credit scoring models using rule extraction from support vector machines[J]．European Journal of Operational Research,2007,183(3):1466-1476
[8] 张永,迟忠先,闫德勤．一种新的模糊补偿多类支持向量机[J]．计算机科学,2006,33(12):152-155
[9] 宋晓东,韩立岩．基于双隶属模糊支持向量机的中小企业信用评价[J]．工业工程,2012,15(1):93-99
[10] 吴冲,夏晗．基于支持向量机集成的电子商务环境下客户信用评估模型研究[J]．中国管理科学,2008,16(10):362-367
[11] Wang G,Ma J．A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine[J]．Expert Systems with Applications,2012,39(5):5325-5331
[12] Zhu X J．Semi-supervised learning literature survey[R]．TR-1530．Madison:University of Wisconsin-Madison,2008
[13] Hs C W,Lin C J．A comparison of methods for multiclass support vector machines[J]．IEEE Transaction on Neural Networks,2002,13(2):415-425
[14] Belkin M,Niyogi P,Sindhwani V．Manifold regularization:a geometric framework for learning from labeled and unlabeled examples[J]．Journal of Machine Learning Research,2006,7:2399-2434
[15] Shalit U,Weinshall D,Chechik G．Online learning in the embedded manifold of low-rank matrices[J]．Journal of Machine Learning Research,2012,13:429-458
[16] Zhao Z K,Qian J S,Cheng J．Semi-supervised kernel based progressive SVM for coal mine gas safety data classification[J]．Journal of Information&Computational Science,2012,9(7):1771-1780
[17] Li Y F,Zhou Z H．Improving semi-supervised support vector machines through unlabeled instances selection[C]∥Procee-dings of AAAI’11．2011:386-391
[18] 郭春花.基于邻域粗糙集和距离判别的信用风险评级[J].重庆理工大学学报:自然科学版,2013,7(2):130-134

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed