众包和垃圾邮件行为检测中的数据质量.
Yang Ba1, Michelle V Mancenido2, Erin K Chiou3
1Ira A. Fulton Schools of Engineering, School of Computing and Augmented Intelligence, Data Science, Analytics and Engineering, Arizona State University, Suite 342AE, 3rd floor 699 S. Mill Avenue, 85281, Tempe, AZ, USA. yangba@asu.edu.
Behavior research methods
|August 8, 2025
概括
这项研究引入了一种新的方法来评估众包数据质量和检测垃圾邮件发送者. 它通过评估注释器的一致性和可信度来增强机器学习,这对于可靠的AI开发至关重要.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 众包对于有效地标记机器学习数据集至关重要.
- 评估群众提供的数据质量对于减少偏见和提高AI性能至关重要.
- 传统的质量指标对于复杂的在线众包场景是不够的.
研究的目的:
- 开发一种系统的方法来评估众包数据质量.
- 检测和分类来自群众工作者的垃圾邮件威胁.
- 为了测量注释者的一致性和可信度,而没有基本真相.
主要方法:
- 差异分解用于数据质量评估和垃圾邮件检测.
- 垃圾邮件发送者被分为三个行为类别.
- 开发一个垃圾邮件索引,以确保整体数据的一致性.
- 使用马尔科夫链和通用随机效应模型来衡量员工信任度.
主要成果:
- 展示了评估众包数据质量的实际框架.
- 提出的方法有效地识别和分类了垃圾邮件发送者.
- 这些技术在使用真实和模拟数据的面部验证任务中被证明是有利的.
结论:
- 开发的系统方法提高了机器学习众包数据的可靠性.
- 准确评估注释者的一致性和可信度是可以实现的,即使没有基础真相.
- 这种方法对于减轻偏见和提高在众包数据上训练的AI模型性能至关重要.
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