Group anomaly detection using deep generative models

Raghavendra Chalapathy, Edward Toth, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies. Code related to this paper is available at: https://github.com/raghavchalapathy/gad, https://www.cs.cmu.edu/~lxiong/gad/gad.html, https://github.com/jorjasso/SMDD-group-anomaly-detection, https://github.com/cjlin1/libsvm.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
EditorsFrancesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim, Michele Berlingerio
PublisherSpringer Verlag
Pages173-189
Number of pages17
ISBN (Print)9783030109240
DOIs
Publication statusPublished - 1 Jan 2019
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018 - Dublin, Ireland
Duration: 10 Sep 201814 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11051 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
CountryIreland
CityDublin
Period10/9/1814/9/18

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Keywords

  • Adversarial
  • Auto-encoders
  • Group anomaly detection
  • Variational

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chalapathy, R., Toth, E., & Chawla, S. (2019). Group anomaly detection using deep generative models. In F. Bonchi, T. Gärtner, N. Hurley, G. Ifrim, & M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings (pp. 173-189). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11051 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-10925-7_11