面向AUC的域调整:从理论到算法
IEEE transactions on pattern analysis and machine intelligence
|August 10, 2023
概括
这项研究引入了面向AUC的无监督域适应 (UDA) 的新方法,以解决域转移问题. 拟议的框架有效地处理分布差异,改善机器学习模型在现实场景中的性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- ROC曲线下的面积 (AUC) 对机器学习至关重要,特别是在疾病预测和使用不平衡数据集检测欺诈中.
- 当训练和测试数据分布不同时,现有的AUC方法通常会失败 (域移).
- 在面向AUC的学习 (AUCUDA) 中解决领域转移是一个公开的挑战.
研究的目的:
- 为以AUC为导向的无监督域调整 (AUCUDA) 提出理论框架和实际解决方案.
- 开发一种方法,克服在域移动下标准AUC学习的局限性.
主要方法:
- 使用AUC的新型分布差异构建了一个通用化约束.
- 开发了一个理论结果,解决AUC风险术语的相互依赖性.
- 建议将伪标签策略整合到一个端到端的培训框架中,以克服注释要求.
主要成果:
- 拟议的概括界限提供了对域转移下的AUC风险更清晰的见解.
- 端到端框架有效地处理域名转移而不需要目标域注释.
- 在五个现实世界数据集上的实证研究证明了框架的有效性.
结论:
- 开发的AUCUDA框架为面临领域转移的机器学习任务提供了强大的解决方案.
- 这项工作为以AUC为导向的域适应研究奠定了基础.
- 提出的方法显示,在各种数据集中,AUC性能显著改善.
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