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A Q-learning approach to attribute reduction

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Abstract

Attribute reduction is a paradigm covering both theories and techniques of selecting required attributes with constraints related to rough set. Currently, though various searching strategies have been developed for achieving such a purpose, few of them take the rewards of identifying attributes into account. In this study, inspired by the popular reinforcement learning mechanism, a Q-learning based procedure is designed to search qualified attributes and then construct the expected reduct. Specifically, state is regarded as the temporary result of selected attributes, action is regarded as variation of such a temporary result if random strategy is performed. Immediately, the reward can be obtained, which offers guidance on identifying attributes with the greatest reward. Moreover, considering the random factors emerge in our scheme, an ensemble device is also used to further improve classification performance of selected attributes in reduct. Finally, comprehensive experiments over a total of 15 UCI datasets clearly validates the superiorities of our study against 5 state-of-the-art approaches.

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Acknowledgment

This work was supported by the Natural Science Foundation of China (Nos. 62076111, 62006128, 62006099, 61906078), the Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province (Nos. OBDMA202104, OBDMA202002).

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China

    Yuxin Liu, Zhice Gong, Keyu Liu & Xibei Yang

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Keyu Liu

  3. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, Jiangsu, China

    Suping Xu

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6R 2V4, AB, Canada

    Suping Xu

  5. School of Information Science and Technology, Nantong University, Nantong, 226000, Jiangsu, China

    Hengrong Ju

  6. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, China

    Xibei Yang

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  1. Yuxin Liu
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  2. Zhice Gong
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  3. Keyu Liu
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Correspondence to Xibei Yang.

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Liu, Y., Gong, Z., Liu, K. et al. A Q-learning approach to attribute reduction. Appl Intell 53, 3750–3765 (2023). https://doi.org/10.1007/s10489-022-03696-w

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