Semi-supervised multi-Layer convolution kernel learning in credit evaluation. (December 2021)

• Record Type: Journal Article
• Authors: Xu, Lixiang; Cui, Lixin; Weise, Thomas; Li, Xinlu; Wu, Zhize; Nie, Feiping; Chen, Enhong; Tang, Yuanyan
• Is Part Of: Pattern recognition. Volume 120(2021)
• Publication Date: 2021-12
• Subjects: Semi-supervised learning -- SVM -- Convolution kernel function -- Random sampling -- Multi-layer kernel; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108125
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We analyze the basic solution of a generalized differential operator. We give a class of convolution kernel function. We propose a semi-supervised multi-layer convolution kernel SVM algorithm. We define two semi-supervised methods: SSMCK-MKL and SSMCK-AO. Abstract: 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.