TY - JOUR A2 - 黄，辰溪AU - 泛爽AU - 卫建国AU - 潘浩PY - 2020 DA - 2020年5月8日TI - 论中国P2P在线贷款基于平台的混合核支持评估模型Vector Machine SP - 4561834 VL - 2020 AB - Accurate evaluation of the risk level and operation performances of P2P online lending platforms is not only conducive to better functioning of information intermediaries but also effective protection of investors’ interests. This paper proposes a genetic algorithm (GA) improved hybrid kernel support vector machine (SVM) with an index system to construct such an evaluation model. A hybrid kernel consisting of polynomial function and radial basis function is improved, specifically kernel parameters and the weight of two kernels, by GA method with excellent global optimization and rapid convergence. Empirical testing based on cross-sectional data from Chinese P2P lending market demonstrates the superiority of the improved hybrid kernel SVM model. The classification accuracy of credit risk level and operation quality is higher than the single kernel SVM model as well as the hybrid kernel model with empirical parameter values. SN - 1058-9244 UR - https://doi.org/10.1155/2020/4561834 DO - 10.1155/2020/4561834 JF - Scientific Programming PB - Hindawi KW - ER -