Distributed lévy sailfish optimizer optimizes XGBoost fraud detection
Hongwei Chen, Lun Chen, Tao Wu, Xueran Wu · 2023
In credit card, shopping, and insurance systems, a small amount of fraud may sometimes exist in the massive transaction data, so fraud detection is widely used in these systems. Extreme Gradient Boost (XGBoost) is outstanding in the competition, many scholars use it in various different fields of classification and prediction. The hyperparameters set by XGBoost determine its performance strength or weakness, and the many hyperparameters make it difficult to have good choices. In this paper, we use the improved Lévy sailfish optimizer (LSFO) to solve the parameter selection problem. As a relatively new intelligent optimization algorithm, the sailfish optimizer chooses spiral updating to optimize the search process of the sailfish and adds Lévy flights to increase its performance. To validate the improved performance, several common optimization algorithms such as PSO, HHO, and WOA are compared using the Lévy Sailfish Optimizer. A fraudulent dataset is used to evaluate the performance of Lévy Sailfish Optimizer Optimization XGBoost (LSFO-XGBoost) in comparison with Decision Tree (DT), Random Forest (RF), SFO-XGBoost and PSO-XGBoost. In LSFO-XGBoost hyperparameter optimization, due to its long training time, the distributed Spark framework is applied to improve its training in a distributed manner. The experimental results show that the proposed LSFO is effective in solving the XGBoost hyperparameter problem, and the distributed LSFO-XGBoost algorithm is better in terms of time consumption and performance.