@inproceedings{ab7de15afb9f4a11ab9229fdb4ba3333,
title = "A novel ensemble learning-based approach for click fraud detection in mobile advertising",
abstract = "By diverting funds away from legitimate partners (a.k.a publishers), click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. As such, fraud detection algorithms which can identify fraudulent behavior based on user click patterns are extremely valuable. Based on the BuzzCity dataset, we propose a novel approach for click fraud detection which is based on a set of new features derived from existing attributes. The proposed model is evaluated in terms of the resulting precision, recall and the area under the ROC curve. A final ensemble model based on 6 different learning algorithms proved to be stable with respect to all 3 performance indicators. Our final model shows improved results on training, validation and test datasets, thus demonstrating its generalizability to different datasets.",
keywords = "Click fraud, ensemble model, feature extraction, skewed data",
author = "Perera, {Kasun S.} and Bijay Neupane and Faisal, {Mustafa Amir} and Zeyar Aung and Woon, {Wei Lee}",
year = "2013",
doi = "10.1007/978-3-319-03844-5_38",
language = "British English",
isbn = "9783319038438",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "370--382",
booktitle = "Mining Intelligence and Knowledge Exploration - First International Conference, MIKE 2013, Proceedings",
note = "1st International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2013 ; Conference date: 18-12-2013 Through 20-12-2013",
}