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Fast fixed granular-ball for attribute reduction in label noise environments and its application in medical diagnosis

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Abstract

Although neighborhood rough set(NRS) based attribute reduction methods have achieved excellent performance in many scenarios, the efficiency and robustness of these methods have not attracted much attention. In this study, we propose a fast fixed granular-ball model (FFGB) for attribute reduction in label noise environments. In FFGB, we propose a fast neighborhood search mechanism to improve the efficiency of NRS. This fast mechanism reduces the neighborhood search range from the universe to a neighborhood and reduces the time complexity of the neighborhood calculation to much less than \(O(n^2)\). Based on the fast mechanism, we propose FFGB model whose definitions are relaxed to be robust to against label noise. In addition, a FFGB attribute reduction algorithm is designed. Finally, we apply the FFGB attribute reduction to medical diagnosis. The experimental results indicate that FFGB is more efficient and robust than the comparison methods.

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Acknowledgements

This work is supported in part by the National Key Research and Development Program of China under Grant 2021YFB3301000, and in part by the Chongqing Talent Plan Project under Grant cstc2021ycjh-bgzxm0206. Doctoral Talent Training Program of Chongqing University of Posts and Telecommunications under Grant No. BYJS202010, the Intelligent Manufacturing Industry Technology Research Institute Open Fund under Grant No. ZNZZ2108, the Key Research and Development Program of Dazhou Science and Technology Bureau under Grant Nos. 20ZDYF0003 and 20ZDYF0001, the Ministry of Education’s Industry School Cooperation Collaborative Education Project under Grant 22097042270822, and by the Multi dimensional data perception and intelligent information processing Dazhou key laboratory project under Grant Nos. DWSJ2202 and DWSJ2207.

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Authors and Affiliations

  1. School of Intelligent Manufacturing, Sichuan University of Arts and Science, Dazhou, 635000, Sichuan, China

    Xiaoli Peng

  2. Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Ping Wang & Jie Qian

  3. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Ping Wang & Jie Qian

  4. School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Yabin Shao

  5. Dazhou Vocational and Technical College, Dazhou, 635000, Sichuan, China

    Yabin Shao & Yuanlin Gong

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  1. Xiaoli Peng
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  2. Ping Wang
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  3. Yabin Shao
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  4. Yuanlin Gong
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  5. Jie Qian
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Correspondence to Ping Wang.

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Peng, X., Wang, P., Shao, Y. et al. Fast fixed granular-ball for attribute reduction in label noise environments and its application in medical diagnosis. Int. J. Mach. Learn. & Cyber. 15, 1039–1054 (2024). https://doi.org/10.1007/s13042-023-01954-y

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