Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection

In traditional supervised learning, the model is trained through a large number of known labels. In reality, the amount of data increases rapidly, and the cost of manual annotation is high. Besides, under the restriction of privacy protection, it is infeasible to obtain labels of a large number of samples. However, the proportion of samples in a certain class can be easily obtained. Thanks to label proportional learning, we can obtain the instance-level classifier merely based on the proportional information. In addition to this, label proportional learning with minor instance-level labeling can potentially improve the accuracy of the model. Hence, in this paper, we propose an improved model based on non-negative matrix factorization to solve semi-supervised label proportional learning, called semi-supervised proportion Matrix Factorization (SPMF). In order to effectively predict car default customers, our method leverage a small number of known labels and the remaining sample label proportional information to construct a classification model. In our experiment, customers defaults can be predicted and high prediction accuracy can be guaranteed. This study is potentially useful for Internet auto financial institutions to properly avoid default risks and establish a more reliable credit rating mechanism. Particularly, our approach provides an alternative way to effectively solve default customer identification under the privacy protection constraint.