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A Hybrid Model Integrating Improved Fuzzy c-means and Optimized Mixed Kernel Relevance Vector Machine for Classification of Coal and Gas Outbursts

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Abstract

The class labels of collected coal and gas outbursts sample data may be wrong, if these collected sample data are directly used for outbursts classification, the accuracy and efficiency are very low. In this paper, a novel hybrid model that integrates fuzzy c-means clustering based on isometric mapping feature extraction and Bayesian optimized mixed kernel relevance vector machine classifier is proposed and applied to improve the classification performance of coal and gas outbursts. First, the isometric mapping is used to extract the non-linear information, then the significant features are selected, in order to improve the classification performance of coal and gas outbursts, the fuzzy c-means is used to perform clustering analysis on the effective features obtained from isometric mapping, mining the structural information and internal regularity of the sample data and estimating classification labels of sample data. Second, A mixed kernel relevance vector machine classifier is proposed to classify coal and gas outbursts, improving the learning and generalization ability of outbursts classification, and the classifier parameters are optimized by Bayesian optimization with global and local search capability remarkably. Finally, the improved fuzzy c-means clustering is integrated into the mixed kernel relevance vector machine classifier model, and Bayesian optimization algorithm is used to help train a better classifier for outbursts classification. The obtained experimental results on the collected actual dataset of coal and gas outbursts show that proposed clustering method can improve the clustering effect and efficiency, decrease the feature vector size up to 50% and achieves the accuracy and running time of 100% and 5.26 s, respectively, which outperforms prior methods with 98% and 5.38 s,the proposed outbursts combined classification model based on classification and clustering model outperforms other methods by 4%-6% with respect to average accuracy. It is believed that the proposed model is very effective for outbursts classification.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (U1704242), the authors would like to thank Hebei Key Laboratory of IOT blockchain integration for support on paper; This paper is the research result of Shijiazhuang city introduction of foreign intelligence project "Research on the application of artificial intelligence technology in the double control mechanism of safe production in Hebei", the subject number is 20220010; and the paper is also the research result of the Hebei Statistical Science Research (plan) funding project "Research on the application of artificial intelligence technology in statistics and governance in Hebei work safety supervision and management", and the subject number is 2021HY29.

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Authors and Affiliations

  1. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Xuzhou, China

    Xuning Liu, Zixian Zhang & Guoying Zhang

  2. Department of Computer Engineering, Shijiazhuang University, Shijiazhuang, China

    Xuning Liu & Genshan Zhang

  3. School of Foreign University, Liaocheng University, Liaocheng, China

    Zixian Zhang

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  1. Xuning Liu
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  2. Zixian Zhang
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  4. Guoying Zhang
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Correspondence to Genshan Zhang or Guoying Zhang.

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Liu, X., Zhang, Z., Zhang, G. et al. A Hybrid Model Integrating Improved Fuzzy c-means and Optimized Mixed Kernel Relevance Vector Machine for Classification of Coal and Gas Outbursts. Neural Process Lett 54, 5615–5641 (2022). https://doi.org/10.1007/s11063-022-10877-8

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