Anti-drifting Feature Selection via Deep Reinforcement Learning (Student Abstract)
Keywords:Reinforcement Learning, Data Stream Mining, Dimensionality Reduction, Feature Selection
AbstractFeature selection (FS) is a crucial procedure in machine learning pipelines for its significant benefits in removing data redundancy and mitigating model overfitting. Since concept drift is a widespread phenomenon in streaming data and could severely affect model performance, effective FS on concept drifting data streams is imminent. However, existing state-of-the-art FS algorithms fail to adjust their selection strategy adaptively when the effective feature subset changes, making them unsuitable for drifting streams. In this paper, we propose a dynamic FS method that selects effective features on concept drifting data streams via deep reinforcement learning. Specifically, we present two novel designs: (i) a skip-mode reinforcement learning environment that shrinks action space size for high-dimensional FS tasks; (ii) a curiosity mechanism that generates intrinsic rewards to address the long-horizon exploration problem. The experiment results show that our proposed method outperforms other FS methods and can dynamically adapt to concept drifts.
How to Cite
Wang, A., Yang, H., Mao, F., Zhang, Z., Yu, Y., & Liu, X. (2023). Anti-drifting Feature Selection via Deep Reinforcement Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16356-16357. https://doi.org/10.1609/aaai.v37i13.27038
AAAI Student Abstract and Poster Program