TY  - CONF
AU  - Lobo, Jesus L.
AU  - Del Ser, Javier
AU  - Osaba, Eneko
A4  - Xue, Bing
A4  - Pechenizkiy, Mykola
A4  - Koh, Yun Sing
PY  - 2021
DO  - 10.1109/ICDMW53433.2021.00045
SN  - 9781665424271
SN  - 2375-9232
UR  - https://hdl.handle.net/11556/2000
AB  - Scenarios dealing with data streams often undergo changes in data distribution, which ultimately lead to a performance degradation of algorithms learning from such data flows (concept drift). This phenomenon calls for the adoption of adaptive learning...
LA  - eng
PB  - IEEE Computer Society
TI  - Lightweight Alternatives for Hyper-parameter Tuning in Drifting Data Streams
TY  - conference output
ER  -