Solve fraud detection problem by using graph based learning methods

Preprint submitted to RGN Publications on 21 /5/2018 Abstract The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards' 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 un - supervised learning techniques and supervised learning technique. In this paper, we employ the graph p - Laplacian based semi - supervised learning methods combi ned with the under - sampling technique such as Cluster Centroids to solve the credit cards' fraud transactions detection problem. Experimental results show that that the graph p - Laplacian semi - supervised learning method s outper form the current state of art graph Laplacian based semi - supervised learning method ( p 2). 2010 AMS Classi fi cation: 05C85 Keywords and phrases: graph p - Laplacian, credit card, fraud detection, semi - supervised learning Article type: Research article 1 Introduction While purchasing online, the transactions can be done by using credit cards that are issued by the bank.