TY - GEN
T1 - Auto-Reg
T2 - 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025
AU - Verma, Nilesh
AU - Bifet, Albert
AU - Pfahringer, Bernhard
AU - Bahri, Maroua
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Automated Machine Learning (AutoML) has revolutionized the development of machine learning pipelines. However, its application to data streams presents unique challenges. While significant progress has been made in streaming classification, advancements in streaming regression remain limited. To address this gap, we propose Auto-Reg, an AutoML framework designed specifically for data stream regression. Auto-Reg introduces two key components: a dynamic budget adjustment mechanism for efficient resource allocation and a Probability-Weighted Hyperparameter Search (PWHS) strategy that balances exploration and exploitation. Comprehensive experiments on both real-world and synthetic datasets, supported by theoretical and empirical evaluations, demonstrate that Auto-Reg consistently outperforms state-of-the-art data stream regression models in terms of predictive accuracy.
AB - Automated Machine Learning (AutoML) has revolutionized the development of machine learning pipelines. However, its application to data streams presents unique challenges. While significant progress has been made in streaming classification, advancements in streaming regression remain limited. To address this gap, we propose Auto-Reg, an AutoML framework designed specifically for data stream regression. Auto-Reg introduces two key components: a dynamic budget adjustment mechanism for efficient resource allocation and a Probability-Weighted Hyperparameter Search (PWHS) strategy that balances exploration and exploitation. Comprehensive experiments on both real-world and synthetic datasets, supported by theoretical and empirical evaluations, demonstrate that Auto-Reg consistently outperforms state-of-the-art data stream regression models in terms of predictive accuracy.
KW - Automated Machine Learning
KW - Data Stream
KW - Regression
UR - https://www.scopus.com/pages/publications/105009864343
U2 - 10.1007/978-981-96-8183-9_20
DO - 10.1007/978-981-96-8183-9_20
M3 - Conference contribution
AN - SCOPUS:105009864343
SN - 9789819681822
T3 - Lecture Notes in Computer Science
SP - 245
EP - 256
BT - Advances in Knowledge Discovery and Data Mining - 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Proceedings
A2 - Wu, Xintao
A2 - Spiliopoulou, Myra
A2 - Wang, Can
A2 - Kumar, Vipin
A2 - Cao, Longbing
A2 - Wu, Yanqiu
A2 - Yao, Yu
A2 - Wu, Zhangkai
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 10 June 2025 through 13 June 2025
ER -