What is it about?
Click fraud is when online ads are intentionally clicked on too many times, causing problems for businesses. This can lead to wrong click numbers and wasted money. To help businesses deal with this issue, we suggest using a machine learning algorithm called stacking. It combines different methods to find the best solution. In our study, we tried four different methods: AdaBoost, Random Forest, Decision Tree, and Logistic Regression. We also used Logistic Regression again as a second step. We tested our approach using a dataset from a big data service platform. We looked at different indicators like accuracy and score to see how well our method worked. The results showed that our stacking algorithm improved accuracy and stayed consistent. In our study, we tried four different methods: AdaBoost, Random Forest, Decision Tree, and Logistic Regression. We also used Logistic Regression again as a second step to complete the stack. We tested our approach using a dataset from a big data service platform. We looked at different indicators like to see how well our method worked. The results showed that our stacking algorithm improved accuracy and stayed consistent and stable. By using this algorithm, businesses can better detect and prevent click fraud. This helps them feel more secure when advertising online and ensures that their ad campaigns give accurate results.
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Why is it important?
Preventing click fraud is crucial for businesses advertising online. Click fraud leads to inaccurate data and wasted advertising funds. By using advanced algorithms like stacking, businesses can detect fraudulent clicks more effectively, ensuring their ad campaigns reach real users and generate genuine engagement. This not only safeguards their investments but also allows for better decision-making and optimization of advertising strategies. Implementing robust click fraud detection methods is essential for businesses to maintain trust, maximize their advertising budgets, and achieve successful marketing outcomes.
Perspectives
Future research in click fraud prediction can explore the use of additional Machine Learning technologies. It is important to consider the impact of laws like GDPR on detecting fraudulent clicks. Optimal data curation, particularly in balancing positive and negative categories, is another aspect worth studying. Additionally, a data-centric approach focusing on data quality rather than predictor performance presents a viable alternative for finding effective solutions.
Nadir Sahllal
Universite Mohammed V Agdal
Read the Original
This page is a summary of: Click fraud prediction by stacking algorithm, Intelligenza Artificiale, June 2023, IOS Press,
DOI: 10.3233/ia-221069.
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