TY - GEN
T1 - Efficient Concept Drift Detection
T2 - 7th International Conference on Information Science and Systems, ICISS 2024
AU - Liu, Zelong
AU - Wang, Pingfan
AU - Jin, Nanlin
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2025/1/31
Y1 - 2025/1/31
N2 - Concept drift in data streams significantly impacts predictive modelling, as the underlying distribution of the training sample evolves, often leading to increased error rates and degraded model performance. When dealing with big data which has many features and data speed is high, traditional concept drift detection methods might delay in analyzing and detecting in time. This paper introduces an improved drift detection model utilizing multivariate analysis methods for feature selection, thereby enhancing the model’s ability to detect drift more efficiently. Our approach analysis various parameters of the data stream to select the most important features and conduct conduct drift detection based on the selected features. Experimental results demonstrate that this feature-selected drift detection model not only maintains classification performance, but also significantly reducing computational overhead. This predictive framework is particularly valuable in scenarios where large data streams require real-time analysis and where computational resources are limited, providing a practical solution for maintaining robust model performance in dynamic environments.
AB - Concept drift in data streams significantly impacts predictive modelling, as the underlying distribution of the training sample evolves, often leading to increased error rates and degraded model performance. When dealing with big data which has many features and data speed is high, traditional concept drift detection methods might delay in analyzing and detecting in time. This paper introduces an improved drift detection model utilizing multivariate analysis methods for feature selection, thereby enhancing the model’s ability to detect drift more efficiently. Our approach analysis various parameters of the data stream to select the most important features and conduct conduct drift detection based on the selected features. Experimental results demonstrate that this feature-selected drift detection model not only maintains classification performance, but also significantly reducing computational overhead. This predictive framework is particularly valuable in scenarios where large data streams require real-time analysis and where computational resources are limited, providing a practical solution for maintaining robust model performance in dynamic environments.
KW - Concept Drift Detection
KW - Data Stream Mining
KW - Feature Selection
KW - Meta feature
UR - http://www.scopus.com/inward/record.url?scp=85218343167&partnerID=8YFLogxK
U2 - 10.1145/3700706.3700721
DO - 10.1145/3700706.3700721
M3 - Conference Proceeding
AN - SCOPUS:85218343167
T3 - ICISS 2024 - Proceedings of the 7th International Conference on Information Science and Systems
SP - 90
EP - 95
BT - ICISS 2024 - Proceedings of the 7th International Conference on Information Science and Systems
PB - Association for Computing Machinery, Inc
Y2 - 14 August 2024 through 16 August 2024
ER -