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In the pay-per-click online advertisement model, fraudulent publishers’ presence is rare than that of genuine publishers. This high-class imbalance between fraudulent and genuine publishers poses a challenge for the accurate classification of fraudsters due to the bias of automated learning models towards the outnumbered class. In this work, an empirical evaluation of significant popular data sampling methods is carried out using nine state-of-the-art learning models for classifying fraudsters in online advertisement. The main objective of this work is to understand the effect of oversampling, under-sampling, and hybrid sampling methods on the performance of various classifiers in click fraud detection. Extensive experiments are performed on the benchmark FDMA-2012 user-click dataset. The performance of each combination of data sampling method and classifiers is validated using average precision, recall, f1-score, and AUC. The results are also compared with the existing state-of-the-art models. The results suggest that adaptive synthetic sampling (ADASYN) oversampling with a gradient tree boosting (GTB) model performs best with an average precision score of 64.32%.

Data Sampling Strategies for Click Fraud Detection Using Imbalanced User Click Data of Online Advertising: An Empirical Review