基于 XG Boost 方法的社交网络异常用户检测技术.

Aiming at the problems of low recall rate and poor running efficiency caused by traditional abnormal accounts detecting algorithms in non-balanced social network datasets, the paper extracted user content, behavior, attributes, and relationship features from social network data sets, selected features using gradient-enhanced ensemble classifier XGBoost algorithm, established classification model,constructed unbalanced data sets and realized the identification of three types of spam accounts. Experimental results show that it improves the recall rate and the F1 value in identification of three types of abnormal users effectively by XGBoost algorithm in binary classification and multiple classification tasks both in the balanced and unbalanced dataset in comparison with the traditional classification methods such as random forest. And with few features selected by XGBoost, the classification algorithm can get the same effect as with all features of samples, which proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]