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  4. Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
 
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2021
Journal Article
Titel

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

Abstract
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
Author(s)
Rueden, Laura von
Mayer, Sebastian
Beckh, Katharina
Georgiev, Bogdan
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Giesselbach, Sven
Heese, Raoul
Kirsch, Birgit
Walczak, Michal
Pfrommer, Julius
Pick, Annika
Ramamurthy, Rajkumar
Garcke, Jochen
Bauckhage, Christian
Schuecker, Jannis
Zeitschrift
IEEE transactions on knowledge and data engineering
DOI
10.1109/TKDE.2021.3079836
File(s)
N-636331.pdf (1.22 MB)
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Tags
  • machine learning

  • prior knowledge

  • expert knowledge

  • Informed

  • hybrid

  • Neuro-Symbolic

  • survey

  • Taxonomy

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