关键词: 点击欺诈, 不平衡, 集成特征选择, Bagging, 随机森林

Abstract: Click fraud in the online advertising network has affected the stable development of online advertising seriously. This paper proposes an online advertisement click fraud detection ensemble method to solve the problems that redundant features will reduce the training efficiency and imbalanced data will cause the decision boundary to be biased. Firstly, the Bagging ensemble method and Synthetic Minority Oversampling Technique（SMOTE） are used to put as many positive samples as possible into the dataset to reduce the influences of too many negative samples. Then, the relevance metrics feature selection algorithm is used to filter out important features and remove redundant features. At last, a random forest algorithm is used to build a click fraud detection model. This method can identify fraud publishers effectively, which meets the requirements for click fraud detection in online advertising.

Key words: click fraud, imbalance, ensemble feature selection, Bagging, random forest