Anti-drifting Feature Selection via Deep Reinforcement Learning (Student Abstract)

Authors

  • Aoran Wang Nanjing University
  • Hongyang Yang Alibaba Group
  • Feng Mao Alibaba Group
  • Zongzhang Zhang Nanjing University
  • Yang Yu Nanjing University
  • Xiaoyang Liu Columbia University

DOI:

https://doi.org/10.1609/aaai.v37i13.27038

Keywords:

Reinforcement Learning, Data Stream Mining, Dimensionality Reduction, Feature Selection

Abstract

Feature 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.

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Published

2023-09-06

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