| 基于SVM的个人房贷信用评估数据分析 |
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| 引用本文： | 王波,迟忠先,郝艳友.基于SVM的个人房贷信用评估数据分析[J].大连民族学院学报,2008,10(1):78-82. |
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| 作者姓名： | 王波 迟忠先 郝艳友 |
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| 作者单位： | 1. 大连民族学院,计算机科学与工程学院,辽宁,大连,116605
2. 大连理工大学计算机科学与工程系,辽宁,大连,116024 |
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| 摘 要： | 采用SVM的序列最小最优化算法（SMO）作为训练算法对商业银行个人房贷信用评估数据进行分析，着重探讨了在个人房贷信用评估中分别应用径向基核函数参数和SMO训练算法中的参数调整对准确度的影响；通过银行实际数据集将该算法与C4．5和神经网络进行了比较，支持向量机对个人信用评估的总精度高于其他两种算法；支持向量机对实际的住房抵押贷款数据进行信用评估效果较好，且参数调整对试验结果有影响。
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| 关 键 词： | 支持向量机(SVM) 序列最小最优化(SMO) 信用评估 |
| 文章编号： | 1009-315X（2008）01-0078-05 |
| 收稿时间： | 2007-03-30 |
| 修稿时间： | 2007年3月30日 |
Individual Housing Loan Credit Scoring Data Analysis Research Based On SVM |
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| WANG Bo,CHI Zhong-xian,HAO Yan-you.Individual Housing Loan Credit Scoring Data Analysis Research Based On SVM[J].Journal of Dalian Nationalities University,2008,10(1):78-82. |
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| Authors: | WANG Bo CHI Zhong-xian HAO Yan-you |
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| Institution: | WANG Bo, CHI Zhong - xian, HAO Yan- you (1. College of Computer Science and Engineering, Dalian Nationalities University, Dalian Liaoning 116605, China; 2. Department of Computer Science and Engineering, Dalian University of Technology, Dalian Liaoning 116024, China) |
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| Abstract: | This paper selects radialbasis funetion(RBF) kernel function in SMO for personal house loan credit evaluation in business bank,emphasizes on discussing the effects of RBF and SMO to accuracy.In comparison with SVM,C4.5 and nearal netroork.SVM has higher evaluation accuraly than the other two methods.SVM had a better credit evaluation effevt to the house mortgage loan,parameter variation will affect the experimental result as well. |
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| Keywords: | support vector machine(SVM) sequential minimal optimization(SMO) credit scoring |
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