The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
Open accessAbstract
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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Cites
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Mastering the game of Go with deep neural networks and tree search
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Cited by
Explainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
Self-citationMachine learning in concrete science: applications, challenges, and best practices
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Integrating Machine Learning with Human Knowledge
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A review of some techniques for inclusion of domain-knowledge into deep neural networks
Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022) · Scientific Reports · doi:10.1038/s41598-021-04590-0 · Cited by 196
A Survey on Explainable Anomaly Detection
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“This paradigm of integrating prior knowledge into machine learning is called Informed Machine Learning [213], which has received increasing attention over the past few years.”
Metadata
- Authors
- von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Walczak, M., Pfrommer, J., Pick, A., Ramamurthy, R., Garcke, J., Bauckhage, C., & Schuecker, J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2021
- Pages
- 1-1
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W3006087551
- Cited by
- 793
- References
- 31
- FWCI
- 46.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., Kirsch, B., Walczak, M., Pfrommer, J., Pick, A., Ramamurthy, R., Garcke, J., Bauckhage, C., & Schuecker, J. (2021). Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2021.3079836
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