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Automated Source Selection for Online Learning
- Project
- 17002 AutoDC
- Type
- New standard
- Description
- Online algorithm for source selection (i.e., feature selection) in the context of online learning.
- Can significantly reduce monitoring and training overhead.
- Description: X. Wang, F. Shahab Samani, and R. Stadler, “Online feature selection for rapid, low-overhead learning in networked systems,” arXiv preprint, 2020.
- Demonstration: X. Wang, F. Shahab Samani, A. Johnsson, R. Stadler: “Online Feature Selection for Low-overhead Learning in Networked Systems,” 2021 17th International Conference on Network and Service Management (CNSM), pp. 1-7. IEEE, 2021.
- Code: X. Wang, “Online stable feature set (OSFS) algorithm implementation,” 2021. [Online]. Available: https://github.com/Xiaoxuan-W/OSFS
- Contact
- Rolf Stadler
- stadler@ee.kth.se
- Technical features
Input(s):
- Candidate data sources.
Main feature(s):
- Automated reduction of data sources for efficient online learning.
Output(s):
- Selected sources.
- Integration constraints
None
- Targeted customer(s)
- Developers
- Researchers
- Conditions for reuse
Public Software license.
- Confidentiality
- Public
- Publication date
- 01-09-2021
- Involved partners
- KTH (Royal Institute of Technology) (SWE)