用软调和函数检测异常标签，提升医疗数据决策可靠性
Conditional anomaly detection with soft harmonic functions
- 基于软调和函数构建非参数化异常检测框架
- 在多个数据集上优于基线方法，准确识别异常标签
- 适合医疗等需高可信标签的现实场景应用
本文研究条件异常检测问题，旨在识别具有异常响应或类别标签的数据实例。提出一种基于软调和解的新型非参数方法，用于估计标签置信度以检测异常标注。通过正则化避免孤立点及分布边界处样本的误判。在多个合成数据集与UCI机器学习数据集上验证了该方法在识别异常标签方面的有效性。进一步在真实电子健康记录数据集上评估，用于发现异常的患者管理决策。
原文摘要 · Abstract (English)
In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method on several synthetic and UCI ML datasets in detecting unusual labels when compared to several baseline approaches. We also evaluate the performance of our method on a real-world electronic health record dataset where we seek to identify unusual patient-management decisions.
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