针对异常检测中的稀有异常数据，提出可迁移的强效算法
Transfer Neyman-Pearson Algorithm for Outlier Detection
- 设计通用元算法，适应异常分布变化
- 在多个神经网络实现中表现优于传统迁移方法
- 适合异常样本稀缺的实际场景
我们研究了异常检测中的迁移学习问题，其中目标异常数据极为稀少。尽管迁移学习在传统平衡分类中已得到广泛研究，但在异常检测及更一般的不平衡分类设置下的迁移问题仍关注较少。本文提出一种通用元算法，理论上对异常分布的多种变化具有强保障，同时具备实际可实施性。进一步探讨该算法的不同实例化形式，如基于多层神经网络的方法，实验表明其性能显著优于现有仅有的传统平衡分类迁移方法的自然扩展。
原文摘要 · Abstract (English)
We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general meta-algorithm which is shown theoretically to yield strong guarantees w.r.t. to a range of changes in abnormal distribution, and at the same time amenable to practical implementation. We then investigate different instantiations of this general meta-algorithm, e.g., based on multi-layer neural networks, and show empirically that they outperform natural extensions of transfer methods for traditional balanced classification settings (which are the only solutions available at the moment).
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