Solve fraud detection problem by using graph based learning methods
This is an incremental improvement for banks and credit card holders to reduce fraud losses.
The paper tackled credit card fraud detection by using graph p-Laplacian semi-supervised learning with undersampling techniques, and experimental results showed it outperformed the state-of-the-art graph Laplacian method (p=2).
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised learning techniques and supervised learning techniques. In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that the graph p-Laplacian semi-supervised learning methods outperform the current state of the art graph Laplacian based semi-supervised learning method (p=2).