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:2019,28(7):133-138
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基于C4.5算法优化SVM的个人信用评估模型
(福州大学 经济与管理学院, 福州 350108)
Evaluation Model for Personal Credit Risk Based on C4.5 Algorithm for Optimizing SVM
(School of Economics and Management, Fuzhou University, Fuzhou 350108, China)
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投稿时间:2018-12-29    修订日期:2019-01-18
中文摘要: 支持向量机作为非参数方法已经广泛应用于信用评估领域.为克服其训练高维数据不能主动进行特征选择导致准确率下降的缺点,构建C4.5决策树优化支持向量机的信用评估模型.利用C4.5信息熵增益率方法进行属性选择,减少冗余属性.模型通过网格搜索确定最优参数,使用F-score和平均准确率评价模型性能,并在两组公开数据集上进行验证.实证分析表明,C4.5决策树优化支持向量机的信用评估模型有效减少了数据学习量,较于传统各类单一模型有较高的分类准确率和实用性.
Abstract:Support Vector Machine (SVM) has been widely used in the field of credit evaluation as non-parametric method. However, it cannot actively select attributes when processing high-dimensional data which may cause a drop in accuracy. In order to overcome this shortcoming, credit evaluation model of C4.5 decision tree optimized SVM is constructed to select attributes, and reduce redundant attributes. The model determines the optimal parameters through grid search, uses F-score and average accuracy to evaluate model performance on two sets of public data sets. Empirical analysis shows that the proposed model effectively reduces data learning process, and has higher classification accuracy and practicability than the various traditional types of single models.
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基金项目:国家自然科学基金(61773123)
引用文本:
刘潇雅,王应明.基于C4.5算法优化SVM的个人信用评估模型.计算机系统应用,2019,28(7):133-138
LIU Xiao-Ya,WANG Ying-Ming.Evaluation Model for Personal Credit Risk Based on C4.5 Algorithm for Optimizing SVM.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):133-138

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