title: Conditional Anomaly Detection with Soft Harmonic Functions.
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.
authors.affiliation: INRIA Lille - Nord Europe, SequeL project, 40 avenue Halley, Villeneuve d'Ascq, France, michal.valko@inria.fr.
authors.forename: Michal
authors.initials: M
authors.lastname: Valko
authors.affiliation: Technicolor Labs, Palo Alto, California, USA, branislav.kveton@technicolor.com.
authors.forename: Branislav
authors.initials: B
authors.lastname: Kveton
authors.affiliation: Computer Science Department, University of Pittsburgh, Pennsylvania, USA, hamed@cs.pitt.edu.
authors.forename: Hamed
authors.initials: H
authors.lastname: Valizadegan
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pennsylvania, USA, gfc@pitt.edu.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.affiliation: Computer Science Department, University of Pittsburgh, Pennsylvania, USA, milos@pitt.edu.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
keywords: backbone graph; conditional anomaly detection; graph methods; harmonic solution; health care informatics; outlier and anomaly detection; random walks