Abstract The user fraud detection is realized by constructing the binary-classification model in the traditional methods, and therefore it is difficult to obtain the potential of applications. In this paper, an algorithm of user credit ranking based on structured non-linear ordinal regression and a robust structured non-linear ordinal regression model are proposed. Firstly, an adaptive global weight matrix is generated to solve overfitting and underfitting caused by the imbalanced distribution of samples. Then, the penalty constraint of ordered inter-categories is established to optimize the projection direction to avoid noises and enhance the robustness of the model. The user information from the actual internet applications is collected, and feature extraction and labelling of ordered categories are conducted. The experiment shows the proposed model achieves better performance.
Fund:National Natural Science Foundation of China(No.61902165,61976109), Natural Science Foundation of Liaoning Pro-vince(No.20180550542), Liaoning Provincial Department of Education Youth Foundation(No.LQ2019029), Dalian Science and Technology Innovation Fund (No.2018J12GX047), Dalian Key Laboratory Special Fund
Corresponding Authors:
ZHANG Jing, Ph.D., lecturer. Her research interests include machine learning and pattern classification.
About author:: REN Yonggong, Ph.D., professor. His research interests include data mining.GUO Jiaqi, master student. Her research interests include machine learning and pattern classification.
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2020, Vol. 33 
