FL-XGBTC:受联合学习启发,利用 XG-boost 调整分类器检测 YouTube 垃圾内容

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY
Vandana Sharma, Anurag Sinha, Ahmed Alkhayyat, Ankit Agarwal, Peddi Nikitha, Sable Ramkumar, Tripti Rathee, Mopuru Bhargavi, Nitish Kumar
{"title":"FL-XGBTC:受联合学习启发,利用 XG-boost 调整分类器检测 YouTube 垃圾内容","authors":"Vandana Sharma, Anurag Sinha, Ahmed Alkhayyat, Ankit Agarwal, Peddi Nikitha, Sable Ramkumar, Tripti Rathee, Mopuru Bhargavi, Nitish Kumar","doi":"10.1007/s13198-024-02502-9","DOIUrl":null,"url":null,"abstract":"<p>The problem of spam content in YouTube comments is an ongoing issue, and detecting such content is a critical task to maintain the quality of user experience on the platform. In this study, we propose a Federated Learning Inspired XG-Boost Tuned Classifier, FL-XGBTC, for YouTube spam content detection. The proposed model leverages the advantages of federated learning, which enables the training of a model collaboratively across multiple devices without sharing raw data. The FL-XGBTC model is based on the XGBoost algorithm, which is a powerful and widely used ensemble learning algorithm for classification tasks. The proposed model was trained on a large and diverse dataset of YouTube comments, which includes both spam and non-spam comments. The results demonstrate that the FL-XGBTC model achieved a high level of accuracy in detecting spam content in YouTube comments, outperforming several baseline models. Additionally, the proposed model provides the benefit of preserving user privacy, which is a critical consideration in modern machine-learning applications. Overall, the proposed Federated Learning Inspired XG-Boost Tuned Classifier provides a promising solution for YouTube spam content detection that leverages the benefits of federated learning and ensemble learning algorithms. The major contribution of this work is to demonstrate and propose a framework for showing a distributed federated classifier for the multiscale classification of youtube spam comments using the Ensemble learning method.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection\",\"authors\":\"Vandana Sharma, Anurag Sinha, Ahmed Alkhayyat, Ankit Agarwal, Peddi Nikitha, Sable Ramkumar, Tripti Rathee, Mopuru Bhargavi, Nitish Kumar\",\"doi\":\"10.1007/s13198-024-02502-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The problem of spam content in YouTube comments is an ongoing issue, and detecting such content is a critical task to maintain the quality of user experience on the platform. In this study, we propose a Federated Learning Inspired XG-Boost Tuned Classifier, FL-XGBTC, for YouTube spam content detection. The proposed model leverages the advantages of federated learning, which enables the training of a model collaboratively across multiple devices without sharing raw data. The FL-XGBTC model is based on the XGBoost algorithm, which is a powerful and widely used ensemble learning algorithm for classification tasks. The proposed model was trained on a large and diverse dataset of YouTube comments, which includes both spam and non-spam comments. The results demonstrate that the FL-XGBTC model achieved a high level of accuracy in detecting spam content in YouTube comments, outperforming several baseline models. Additionally, the proposed model provides the benefit of preserving user privacy, which is a critical consideration in modern machine-learning applications. Overall, the proposed Federated Learning Inspired XG-Boost Tuned Classifier provides a promising solution for YouTube spam content detection that leverages the benefits of federated learning and ensemble learning algorithms. The major contribution of this work is to demonstrate and propose a framework for showing a distributed federated classifier for the multiscale classification of youtube spam comments using the Ensemble learning method.</p>\",\"PeriodicalId\":14463,\"journal\":{\"name\":\"International Journal of System Assurance Engineering and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of System Assurance Engineering and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13198-024-02502-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02502-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

YouTube 评论中的垃圾内容是一个持续存在的问题,检测此类内容是保持平台用户体验质量的关键任务。在本研究中,我们提出了一种受联合学习启发的 XG-Boost 调整分类器 FL-XGBTC,用于 YouTube 垃圾内容检测。所提出的模型利用了联合学习的优势,可以在不共享原始数据的情况下跨多个设备协同训练模型。FL-XGBTC 模型基于 XGBoost 算法,这是一种功能强大且广泛用于分类任务的集合学习算法。提出的模型是在一个大型、多样化的 YouTube 评论数据集上进行训练的,其中包括垃圾评论和非垃圾评论。结果表明,FL-XGBTC 模型在检测 YouTube 评论中的垃圾内容方面达到了很高的准确率,优于几个基准模型。此外,所提出的模型还具有保护用户隐私的优势,而这正是现代机器学习应用的一个重要考虑因素。总之,所提出的受联合学习启发的 XG-Boost 调整分类器为 YouTube 垃圾内容检测提供了一种很有前途的解决方案,它充分利用了联合学习和集合学习算法的优势。这项工作的主要贡献在于展示并提出了一个框架,利用集合学习方法展示了一种分布式联盟分类器,用于对 YouTube 垃圾评论进行多尺度分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection

FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection

The problem of spam content in YouTube comments is an ongoing issue, and detecting such content is a critical task to maintain the quality of user experience on the platform. In this study, we propose a Federated Learning Inspired XG-Boost Tuned Classifier, FL-XGBTC, for YouTube spam content detection. The proposed model leverages the advantages of federated learning, which enables the training of a model collaboratively across multiple devices without sharing raw data. The FL-XGBTC model is based on the XGBoost algorithm, which is a powerful and widely used ensemble learning algorithm for classification tasks. The proposed model was trained on a large and diverse dataset of YouTube comments, which includes both spam and non-spam comments. The results demonstrate that the FL-XGBTC model achieved a high level of accuracy in detecting spam content in YouTube comments, outperforming several baseline models. Additionally, the proposed model provides the benefit of preserving user privacy, which is a critical consideration in modern machine-learning applications. Overall, the proposed Federated Learning Inspired XG-Boost Tuned Classifier provides a promising solution for YouTube spam content detection that leverages the benefits of federated learning and ensemble learning algorithms. The major contribution of this work is to demonstrate and propose a framework for showing a distributed federated classifier for the multiscale classification of youtube spam comments using the Ensemble learning method.

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
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信