Enhancing performance of a LENN based CCFD model with a hybrid TOPSIS-ReliefF based feature selection technique
Rajashree Dash, Rasmita Dash, Rasmita Rautray · 2022
The rapid acceleration of transaction digitization has paved a comfortable path for fraudsters to personate deviant frauds. The loss from credit card fraud on its own is causing massive destruction in the country's revenue. Impelling effective credit card fraud detection (CCFD) models that can handle the changing nature of fraud is one way to minimize this loss. One of the fundamental challenges in developing such models is to identify the relevant input features those are informative in determining the fraud cases. In this article, a hybrid feature selection technique is suggested in order to lift the performance of a Legender Polynomial Neural Network (LENN) based CCFD model. For identifying the optimal features for the model using the hybrid approach, initially the rank and weights of each feature is calculated using a ReliefF algorithm and then the optimal feature size is decided using a TOPSIS based multi criteria decision making approach. The performance of the CCFD model with and without feature selection approach is accessed over two credit card datasets by considering five classification metric