In many practical credit evaluation problems, a lot of manpower as well as financial and material resources are required to label samples. Therefore, in the process of labeling, only a small number of samples with category labels can be obtained to train classification models and a large number of customer samples is abandoned without category labels. To solve this problem, we introduce a semi-supervised support vector machine (SVM) technology and combines it with a multi-layer convolution kernel to construct a semi-supervised multi-layer convolution kernel SVM (SSMCK) for category customer credit assessment data sets. We first use a basic solution of the generalized differential operator to generate a base convolution kernel function in the $H^1$ space, and then use the multi-layer strategy of deep learning to construct the multi-layer convolution kernel in the $H^2$ and $H^3$ space (called the family of multi-layer convolution kernel) by using the kernel functions in the $H^1$ space. We further propose a semi-supervised multi-layer convolution kernel SVM algorithm based on the category center estimation and develop two novel SSMCK methods to improve the classification ability: the SSMCK based on multi-kernel learning (SSMCK-MKL) and the SSMCK based on alternative optimization (SSMCK-AO). Finally, experimental verification and analysis is carried out on three customer credit evaluation data sets. The results show that our methods outperforms or are comparable to some the state-of-the-art credit evaluation models.

在许多实际的信用评价问题中，需要大量的人力、财力、物力对样本进行标注。因此，在标注的过程中，只能得到少量带有类别标签的样本来训练分类模型，而大量的没有类别标签的客户样本被丢弃。为了解决这个问题，我们引入了半监督支持向量机（SVM）技术，并结合多层卷积核构建了一个半监督多层卷积核支持向量机（SSMCK），用于类别客户信用评估数据集. 我们首先使用广义微分算子的基本解在$H^1$ 空间，然后使用深度学习的多层策略构建多层卷积核 $H^2$ 和 $H^3$ 空间（称为多层卷积核家族）通过使用核函数 $H^1$空间。我们进一步提出了一种基于类别中心估计的半监督多层卷积核 SVM 算法，并开发了两种新的 SSMCK 方法来提高分类能力：基于多核学习的 SSMCK (SSMCK-MKL) 和基于替代优化（SSMK-AO）。最后，对三个客户信用评价数据集进行了实验验证和分析。结果表明，我们的方法优于或可与一些最先进的信用评估模型相媲美。