对推的短信垃圾邮件检测数据集的关键见解
Mohammad Firdaus Johari1, Kang Leng Chiew2, Abdul Razak Hosen1
1Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, 94300, Malaysia.
Scientific reports
|March 10, 2025
概括
本研究使用机器学习模型评估SMS垃圾邮件检测数据集. 由于其复杂性和定性特征,建议在未来的研究中使用数据集5,促进强大的垃圾邮件检测.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 短信服务 (SMS) 垃圾邮件带来了重大安全风险,包括金融欺诈和网络鱼.
- 现有的短信垃圾邮件检测模型通常依赖于各种数据集,但其有效性和影响被低估.
研究的目的:
- 评估十个短信垃圾邮件检测数据集的性能.
- 根据准确性和定性因素评估数据集,以改进短信垃圾邮件检测模型.
主要方法:
- 采用决策树和多项性内贝叶斯模型来评估十个短信垃圾邮件检测数据集.
- 评估数据集的准确性,真实性,类不平衡,功能多样性,元数据和预处理需求.
- 由于数据集的多语言性,对英语和非英语止词删除组进行了实验.
主要成果:
- 由于特征多样性,现实世界的复杂性和平衡的类,数据集5获得了高质量评分 (3.8/5.0).
- 多项素朴贝叶斯在数据集5上显示了86.10%的检测率,表明这是一个具有挑战性但有信息性的数据集.
- 质量评估在数据集评估中非常重要,超出了纯粹的准确度指标.
结论:
- 建议将数据集5用于未来的短信垃圾邮件检测研究,以促进更强大,更适应的模型.
- 选择具有高定性分数的数据集可以提高研究质量,模型通用性,并减轻偏差.
- 具有挑战性的数据集鼓励开发能够处理各种噪音和模两可的垃圾邮件检测系统.
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