An Efficient Method for Identifying Users across Various Digital Devices using the Modified XGboost Algorithm
In the present study, the modified XGBoost algorithm is introduced for identifying users across different digital devices. Then, it is implemented on a dataset and the results are compared with the results of the decision tree, support-vector machine and K-nearest neighbor algorithms. Using several experiments with different parameters, it is proved that using 200 leaves and 1000 trees, the best result is achieved, which is the accuracy rate of 99.94% and the corresponding running time is 5439.26 seconds. The running time of the proposed algorithm is much greater as compared to the other algorithms tested in the present study, so, it shows poor performance in running time. But what makes this algorithm significant is its accuracy. According to the results, its accuracy rate is nearly 100% for any number of input data. The proposed algorithm shows that it can provide reliable results in the case of offline running where it does not matter how much its computing speed is.