Automated Source Selection for Online Learning

• 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

Input(s):

• Candidate data sources.

Main feature(s):

• Automated reduction of data sources for efficient online learning.

Output(s):

• Selected sources.

None

• Developers
• Researchers

Public Software license.