Explainable Machine Learning for Scientific Insights and Discoveries
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scient...
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| Vydáno v: | IEEE access Ročník 8; s. 42200 - 42216 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2169-3536, 2169-3536 |
| On-line přístup: | Získat plný text |
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| Abstract | Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article, we review explainable machine learning in view of applications in the natural sciences and discuss three core elements that we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas. |
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| AbstractList | Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article, we review explainable machine learning in view of applications in the natural sciences and discuss three core elements that we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas. |
| Author | Duarte, Marco F. Bohn, Bastian Roscher, Ribana Garcke, Jochen |
| Author_xml | – sequence: 1 givenname: Ribana surname: Roscher fullname: Roscher, Ribana organization: Institute of Geodesy and Geoinformation, University of Bonn, Bonn, Germany – sequence: 2 givenname: Bastian surname: Bohn fullname: Bohn, Bastian organization: Institute for Numerical Simulation, University of Bonn, Bonn, Germany – sequence: 3 givenname: Marco F. surname: Duarte fullname: Duarte, Marco F. organization: Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA, USA – sequence: 4 givenname: Jochen orcidid: 0000-0002-8334-3695 surname: Garcke fullname: Garcke, Jochen email: jochen.garcke@scai.fraunhofer.de organization: Institute for Numerical Simulation, University of Bonn, Bonn, Germany |
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| CODEN | IAECCG |
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| Title | Explainable Machine Learning for Scientific Insights and Discoveries |
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