Research on click fraud prediction based on multi-algorithm fusion

Ganglong Duan; Jianjun Liu; Weiwei Kong; Bowen Cui; Jiahao Li

doi:10.1117/12.2680157

31 May 2023 Research on click fraud prediction based on multi-algorithm fusion

Ganglong Duan, Jianjun Liu, Weiwei Kong, Bowen Cui, Jiahao Li

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 127043P (2023) https://doi.org/10.1117/12.2680157
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

The detection of click fraud in online advertisements on the Internet for the purpose of extracting advertising fees is one of the important aspects of machine learning applications. In this paper, using the data information of 400000 ad click cheating cases, we use recursive feature elimination method to determine the predictors and use five algorithms of gradient boosted decision tree (GBDT), random forest (RF), Adaboost, KNN and LGbmclassifier to train a single classifier, compare the prediction performance of each type of classifier, and the first three with better prediction performance The top three with better prediction performance were fused with multiple algorithms for prediction. The experimental results show that the random forest, Lgbmclassifier and Adaboost algorithms have the highest prediction accuracy, 87%, 83% and 79%, respectively, with AUC values of 0.90, 0.87 and 0.81. The prediction accuracy of the multi-algorithm fusion model taken in this paper can improve by 3% compared to the single algorithm with the best prediction performance, reaching 90%.

Citation Download Citation

Ganglong Duan, Jianjun Liu, Weiwei Kong, Bowen Cui, and Jiahao Li "Research on click fraud prediction based on multi-algorithm fusion", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 127043P (31 May 2023); https://doi.org/10.1117/12.2680157

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