点击堆叠算法欺诈预测

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2023-06-07 DOI:10.3233/IA-221069
N. Sahllal, E. M. Souidi
{"title":"点击堆叠算法欺诈预测","authors":"N. Sahllal, E. M. Souidi","doi":"10.3233/IA-221069","DOIUrl":null,"url":null,"abstract":"Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method’s premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China’s largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability.","PeriodicalId":42055,"journal":{"name":"Intelligenza Artificiale","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Click fraud prediction by stacking algorithm\",\"authors\":\"N. Sahllal, E. M. Souidi\",\"doi\":\"10.3233/IA-221069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method’s premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China’s largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability.\",\"PeriodicalId\":42055,\"journal\":{\"name\":\"Intelligenza Artificiale\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intelligenza Artificiale\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/IA-221069\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligenza Artificiale","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/IA-221069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

点击欺诈是一种故意夸大网络广告流量的欺骗行为。对于在网上做广告的企业来说,点击欺诈可能经常发生,导致错误的点击统计和资金损失。这就是为什么许多企业不愿在网站和移动应用程序上为自己的产品做广告的原因。为了安全地营销他们的产品,企业需要一种可靠的技术来检测点击欺诈。在本文中,我们提出了一个堆叠算法来解决这个问题。所提出的方法的前提是将多个学习者结合起来以获得最佳结果。选择了合成少数过采样技术(SMOTE)和欠采样的组合来处理不平衡的数据集。在一级学习器中,有四种有监督的机器学习算法,分别是AdaBoost、随机森林、决策树和逻辑回归。此外,逻辑回归被再次用作二级学习者。为了验证所建议方法的有效性,在中国最大的独立大数据服务平台TalkingData的Kaggle上的公共数据集上进行了比较测试。多个指标,如准确度、F1评分、ROC曲线、损失日志和AUC评分,用于分析预测结果。研究结果表明,叠加方法提高了预测精度,同时保持了较高的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Click fraud prediction by stacking algorithm
Click fraud is the sort of deception in which traffic figures for online ads are intentionally inflated. For businesses that advertise online, click fraud may occur often, resulting in erroneous click statistics and lost funds. That is why many businesses are hesitant to advertise their products on websites and mobile apps. To market their products safely, businesses need a reliable technique for detecting click fraud. In this paper we present a stacking algorithm as a solution to this problem. The proposed method’s premise is to combine multiple learners to achieve an optimal result. The Synthetic Minority Oversampling Technique (SMOTE) with a combination of undersampling are chosen to handle the unbalanced dataset. In the first-level learners, there are four supervised Machine Learning algorithms, which are AdaBoost, Random Forest, Decision Tree and Logistic Regression. Moreover, Logistic Regression is used again as a the second-level learner. To verify the efficacy of the suggested approach, comparative tests are carried out on the public dataset available on Kaggle from China’s largest independent big data service platform TalkingData. Multiple indicators, such as Accuracy, F1 Score, ROC curve, Loss Log and AUC Score, are utilized to analyze the prediction outcomes. The findings reveal that the stacking method improves forecast accuracy while also maintaining a high level of stability.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
期刊最新文献
Special Issue NL4AI 2022: Workshop on natural language for artificial intelligence User-centric item characteristics for personalized multimedia systems: A systematic review Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework A framework for safe decision making: A convex duality approach Grounding End-to-End Pre-trained architectures for Semantic Role Labeling in multiple languages
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1