TY - GEN
T1 - CWIN
T2 - 30th IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2023
AU - Bhat, Ranjan
AU - Prabhu, Sharanya
AU - Bayyapu, Neelima
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Due to the dynamic nature of data streams, concept drift detection is a crucial feature for any live data analytics algorithm. We propose CWIN, a novel window-based drift detection technique that exploits the two-sample location-scale Cucconi test. Preliminary results show that CWIN surpasses the state-of-the-art KSWIN in 10 of 12 data streams tested and is better at successfully detecting concept drifts.
AB - Due to the dynamic nature of data streams, concept drift detection is a crucial feature for any live data analytics algorithm. We propose CWIN, a novel window-based drift detection technique that exploits the two-sample location-scale Cucconi test. Preliminary results show that CWIN surpasses the state-of-the-art KSWIN in 10 of 12 data streams tested and is better at successfully detecting concept drifts.
UR - https://www.scopus.com/pages/publications/85193066494
UR - https://www.scopus.com/pages/publications/85193066494#tab=citedBy
U2 - 10.1109/HiPCW61695.2023.00016
DO - 10.1109/HiPCW61695.2023.00016
M3 - Conference contribution
AN - SCOPUS:85193066494
T3 - Proceedings - 2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2023
SP - 55
EP - 56
BT - Proceedings - 2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 December 2023 through 21 December 2023
ER -