TY - GEN
T1 - Dynamic Ensemble Member Selection for Data Stream Classification
AU - Sun, Yibin
AU - Pfahringer, Bernhard
AU - Gomes, Heitor Murilo
AU - Bifet, Albert
N1 - Publisher Copyright:
© 2025 ACM.
PY - 2025/11/10
Y1 - 2025/11/10
N2 - Ensemble methods are widely recognized for their effectiveness in data stream classification. This paper introduces Dynamic Ensemble Member Selection (DEMS), a novel framework that dynamically selects a subset of classifiers from an ensemble for each individual prediction. DEMS ranks base learners based on estimated accuracy and predictive margin, using only the top-K members for prediction, where K is optimized in a self-adaptive manner. The proposed method significantly enhances predictive performance across various state-of-the-art ensemble algorithms, including Streaming Random Patches, Adaptive Random Forest, and Online Smooth Boost. Experimental results demonstrate that DEMS consistently improves classification accuracy while maintaining a minimal runtime overhead of just 11.66% compared to the original methods. This work highlights the potential of DEMS in adapting to concept drift and optimizing ensemble diversity, offering a practical solution for real-time data stream classification.
AB - Ensemble methods are widely recognized for their effectiveness in data stream classification. This paper introduces Dynamic Ensemble Member Selection (DEMS), a novel framework that dynamically selects a subset of classifiers from an ensemble for each individual prediction. DEMS ranks base learners based on estimated accuracy and predictive margin, using only the top-K members for prediction, where K is optimized in a self-adaptive manner. The proposed method significantly enhances predictive performance across various state-of-the-art ensemble algorithms, including Streaming Random Patches, Adaptive Random Forest, and Online Smooth Boost. Experimental results demonstrate that DEMS consistently improves classification accuracy while maintaining a minimal runtime overhead of just 11.66% compared to the original methods. This work highlights the potential of DEMS in adapting to concept drift and optimizing ensemble diversity, offering a practical solution for real-time data stream classification.
KW - classification
KW - data streams
KW - diversity
KW - dynamic ensemble selection
KW - ensemble learning
KW - self-adapting
UR - https://www.scopus.com/pages/publications/105023169904
U2 - 10.1145/3746252.3761072
DO - 10.1145/3746252.3761072
M3 - Conference contribution
AN - SCOPUS:105023169904
T3 - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
SP - 2821
EP - 2831
BT - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery, Inc
T2 - 34th ACM International Conference on Information and Knowledge Management, CIKM 2025
Y2 - 10 November 2025 through 14 November 2025
ER -