Anti-drifting Feature Selection via Deep Reinforcement Learning (Student 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.

Authors

• Aoran Wang Nanjing University
• Hongyang Yang Alibaba Group
• Feng Mao Alibaba Group
• Zongzhang Zhang National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China
• Yang Yu School of Artificial Intelligence, Jilin University, Changchun Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University, Changchun
• Xiaoyang Liu Shanghai Jiao Tong University

Keywords

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