Conditional anomaly detection with soft harmonic functions

TL;DR

This paper introduces a non-parametric method for conditional anomaly detection using soft harmonic functions, effectively identifying unusual labels and decisions in synthetic, UCI, and real-world datasets.

Contribution

A novel soft harmonic-based approach for conditional anomaly detection that estimates label confidence and regularizes to improve detection accuracy.

Findings

Effective in detecting unusual labels in synthetic and UCI datasets.

Successfully identifies atypical patient-management decisions in healthcare data.

Outperforms baseline methods in anomaly detection tasks.

Abstract

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.