Analysis of the publisher's behavior plays a vital role in identifying fraudulent publishers in the pay-per-click model of online advertising. However, the vast amount of raw user click data with missing values pose a challenge in analyzing the conduct of publishers. The presence of high cardinality in categorical attributes with multiple possible values has further aggrieved the issue.
In this paper, gradient tree boosting (GTB) learning is used to address the challenges encountered in learning the publishers' behavior from raw user click data and effectively classifying fraudulent publishers.
The results demonstrate that the GTB effectively classified fraudulent publishers and exhibited significantly improved performance as compared to other learning methods in terms of average precision (60.5 %), recall (57.8 %) and f-measure (59.1%).
The experiments were conducted using publicly available multiclass raw user click dataset and eight other imbalanced datasets to test the GTB's generalizing behavior, while training and testing were done using 10-fold cross-validation. The performance of GTB was evaluated using average precision, recall and f-measure. The performance of GTB learning was also compared with eleven other state-of-the-art individual and ensemble classification models.
