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FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification

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Abstract

Granular computing (GrC) is an essential tool to solve human real problem since the information granules is close to human perception schemes. In GrC, both classification accuracy and interpretability play significant roles. Fuzzy rule (FR) based classification systems are effective methods solving this problem. However, the accuracy of FR may be decreased when solving some complex application. In this paper, a novel model called FR–KDE integrating the FR and kernel density estimation (KDE) in the framework of Dempster–Shafer evidence theory is proposed to deal with the classification problem. By fusing the result of FR and KDE via the Dempster’s combination rule, it can reduce the uncertainty of FR and obtain better accuracy. To illustrate the effect of the FR–KDE approach, it is applied to the medical data classification problem. Experimentally, the results demonstrate that the FR–KDE method is effective in handling biomedical data classification problems.

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Acknowledgements

The authors greatly appreciate the reviewers suggestions and the editor encouragement. This research is funded by the Research Project of Education and Teaching Reform in Southwest University (No. 2019JY053), Fundamental Research Funds for the Central Universities (No. XDJK2019C085) and Chongqing Overseas Scholars Innovation Program (No. cx2018077).

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  1. Xingjian Song and Bowen Qin are joint first authors and they contributed equally to this work.

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  1. School of Computer and Information Science, Southwest University, No. 2 Tiansheng Road, BeiBei District, Chongqing, 400715, China

    Xingjian Song, Bowen Qin & Fuyuan Xiao

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  1. Xingjian Song
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Song, X., Qin, B. & Xiao, F. FR–KDE: A Hybrid Fuzzy Rule-Based Information Fusion Method with its Application in Biomedical Classification. Int. J. Fuzzy Syst. 23, 392–404 (2021). https://doi.org/10.1007/s40815-020-00957-z

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