| 基于支持向量机集成的个人信用评估研究 |
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| 引用本文： | 刘潇雅,王应明. 基于支持向量机集成的个人信用评估研究[J]. 计算机工程与应用, 2020, 56(14): 272-278. DOI: 10.3778/j.issn.1002-8331.1905-0086 |
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| 作者姓名： | 刘潇雅 王应明 |
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| 作者单位： | 福州大学 经济与管理学院，福州 350116 |
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| 基金项目： | 国家自然科学基金面上项目 |
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| 摘 要： | 信用评估模型的优劣会对信贷机构损益和金融市场秩序产生直接的影响,为提升个人信用评估模型的精度,将集成方法应用到信用评估领域,提出改进DS证据理论的支持向量机集成个人信用评估模型,并将属性约减纳入建模过程中。利用C4.5决策树约减冗余属性,并根据数据集类别标签和支持向量机混淆矩阵,后验概率构造证据理论概率赋值函数。计算基于分类器间支持度的权重与专家权重修正由于训练过程受到干扰而产生的冲突证据。通过DS融合做出最终决策。实证分析探讨了该方法的优越性和可行性,可成为一种有效信用评估工具。
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| 关 键 词： | 个人信用评估 支持向量机 改进DS证据理论 属性约减 |
Research on Personal Credit Scoring Based on SVM Ensemble |
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| LIU Xiaoya,WANG Yingming. Research on Personal Credit Scoring Based on SVM Ensemble[J]. Computer Engineering and Applications, 2020, 56(14): 272-278. DOI: 10.3778/j.issn.1002-8331.1905-0086 |
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| Authors: | LIU Xiaoya WANG Yingming |
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| Affiliation: | School of Economics & Management, Fuzhou University, Fuzhou 350116, China |
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| Abstract: | The effectiveness of credit scoring model will have a direct impact on credit institution’s gain and financial market’s order. In order to further improve the accuracy of credit evaluation model, an improving method of credit scoring combing Support Vector Machine（SVM） and improved DS evidence theory is proposed which also incorporates attribute reduction into modeling process. After reducing the redundancy attribute by C4.5 decision tree, the Basic Probability Assignment（BPA） of DS evidence is established based on the category label, confusion matrices and posterior probability of SVMs. Then it calculates the weight based on support between classifiers and expert to correct conflict evidence which produced during the process of training SVMs. Finally it makes final decision according to improved DS evidence theory and SVM ensemble. It is proved that the proposed method is stable, highly accurate, strong robust and feasible. It can be a useful tool for credit scoring. |
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| Keywords: | personal credit scoring support vector machine improved DS evidence theory feature reduction |
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