Abstract: Click fraud detection in online advertising is one of the most important applications of machine learning.Support vector machine (SVM) is a prominent supervised machine learning algorithm on classification problems with roughly equal distributions datasets.However,when applied to click fraud detection problems,the success of SVM is greatly limited due to the extreme imbalanced distribution of FDMA2012 competition dataset.In this paper,three data preprocess methods,random under-sample (RUS),synthetic minority over-sampling technique (SMOTE) and SMOTE+edited nearest neighbor(ENN),were detailed investigated,followed by SVM classifier to solve the question.Results show that the method combining SMOTE＋ENN with SVM achieves accuracy about 95% on minority samples,which basically reaches the requirements of online advertising click fraud detection system.

Key words: Click fraud, Imbalanced, Mixed-sampling, SVM