组合核支持向量机个人信用评估模型

Personal Credit Evaluation Model Based on Combination Kernel Support Vector Machine

针对支持向量机模型在分类问题中的广泛应用,提出了一种新的基于支持向量机的个人信用评估模型,通过对支持向量机直方图交叉核、热核特征核、杰卡德距离核和余弦广义距离核4种核函数的组合处理,构造了投票矩阵;通过实际数据实验,获得了良好的分类结果,同时证明了支持向量机自适应组合核加权模型在信用评分系统中具有良好的性能;因此,这种基于支持向量机的个人信用评估模型可以帮助银行或贷款人做出正确的决策。

Aiming at the wide application of support vector machine(SVM)model in classification,a new personal credit evaluation model based on SVM is proposed.The voting matrix is constructed by combining four kinds of kernels,namely,histogram cross kernels,thermonuclear feature kernels,Jacquard distance kernels and cosine generalized distance kernels.Through the actual data experiment,we get good classification results,and prove that the support vector machine adaptive combination kernel weighting model has good performance in the credit scoring system.Therefore,this personal credit evaluation model based on support vector machine can really help banks or lenders make correct decisions.