Conditional Anomaly Detection with Soft Harmonic Functions
Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory Cooper and Milos Hauskrecht
In: International Conference on Data Mining 2011, 12 Dec - 14 Dec 2011, Vancouver, Canada.
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.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Michal Valko|
|Deposited On:||08 December 2011|