Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
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 structure...
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| Vydané v: | IEEE transactions on knowledge and data engineering Ročník 35; číslo 1; s. 614 - 633 |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1041-4347, 1558-2191 |
| On-line prístup: | Získať plný text |
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| 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. |
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| AbstractList | 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 | Beckh, Katharina Georgiev, Bogdan Kirsch, Birgit Schuecker, Jannis Mayer, Sebastian Bauckhage, Christian Pick, Annika Pfrommer, Julius von Rueden, Laura Heese, Raoul Walczak, Michal Giesselbach, Sven Ramamurthy, Rajkumar Garcke, Jochen |
| Author_xml | – sequence: 1 givenname: Laura orcidid: 0000-0002-7186-9753 surname: von Rueden fullname: von Rueden, Laura email: laura.von.rueden@iais.fraunhofer.de organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 2 givenname: Sebastian orcidid: 0000-0002-1909-7805 surname: Mayer fullname: Mayer, Sebastian organization: Fraunhofer SCAI, Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany – sequence: 3 givenname: Katharina orcidid: 0000-0002-7824-6647 surname: Beckh fullname: Beckh, Katharina organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 4 givenname: Bogdan orcidid: 0000-0002-4687-768X surname: Georgiev fullname: Georgiev, Bogdan organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 5 givenname: Sven orcidid: 0000-0002-2691-1406 surname: Giesselbach fullname: Giesselbach, Sven organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 6 givenname: Raoul orcidid: 0000-0001-7479-3339 surname: Heese fullname: Heese, Raoul organization: Fraunhofer ITWM, Institute for Industrial Mathematics, Kaiserslautern, Germany – sequence: 7 givenname: Birgit orcidid: 0000-0002-4888-4947 surname: Kirsch fullname: Kirsch, Birgit organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 8 givenname: Julius orcidid: 0000-0002-4204-6758 surname: Pfrommer fullname: Pfrommer, Julius organization: Fraunhofer IOSB, Institute for Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany – sequence: 9 givenname: Annika orcidid: 0000-0002-9290-2487 surname: Pick fullname: Pick, Annika organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 10 givenname: Rajkumar orcidid: 0000-0003-4440-7032 surname: Ramamurthy fullname: Ramamurthy, Rajkumar organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 11 givenname: Michal orcidid: 0000-0001-5151-7462 surname: Walczak fullname: Walczak, Michal organization: Fraunhofer ITWM, Institute for Industrial Mathematics, Kaiserslautern, Germany – sequence: 12 givenname: Jochen orcidid: 0000-0002-8334-3695 surname: Garcke fullname: Garcke, Jochen organization: Fraunhofer SCAI, Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany – sequence: 13 givenname: Christian orcidid: 0000-0001-6615-2128 surname: Bauckhage fullname: Bauckhage, Christian organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany – sequence: 14 givenname: Jannis orcidid: 0000-0001-6203-1126 surname: Schuecker fullname: Schuecker, Jannis organization: Fraunhofer IAIS, Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany |
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| Title | Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems |
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