• English
  • Deutsch
  • Log In
    Password Login
    or
  • Research Outputs
  • Projects
  • Researchers
  • Institutes
  • Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders
 
  • Details
  • Full
Options
2020
Conference Paper
Titel

From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders

Abstract
Imbalanced datasets pose severe challenges in training well performing classifiers. This problem is also prevalent in the domain of outlier detection since outliers occur infrequently and are generally treated as minorities. One simple yet powerful approach is to use autoencoders which are trained on majority samples and then to classify samples based on the reconstruction loss. However, this approach fails to classify samples whenever reconstruction errors of minorities overlap with that of majorities. To overcome this limitation, we propose an adversarial loss function that maximizes the loss of minorities while minimizing the loss for majorities. This way, we obtain a well-separated reconstruction error distribution that facilitates classification. We show that this approach is robust i n a wide variety of settings, such as imbalanced data classification or outlier- and novelty detection.
Author(s)
Lübbering, Max
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Ramamurthy, Rajkumar
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Gebauer, Michael
PriceWaterhouseCoopers GmbH
Bell, Thiago
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Sifa, Rafet
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Bauckhage, Christian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hauptwerk
Artificial Neural Networks and Machine Learning - ICANN 2020. Proceedings. Pt.I
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Artificial Neural Networks (ICANN) 2020
Thumbnail Image
DOI
10.1007/978-3-030-61609-0_3
Language
English
google-scholar
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Tags
  • Autoencoder

  • outlier detection

  • imbalanced datasets

  • adversarial training

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Send Feedback
© 2022