Abstract
More than 90% accidents occurred in coal mine are caused by unsafe behaviors of human. How to effectively identify unsafe behaviors and decrease the possibility of their occurrence is the fundamental of avoiding accidents. However, the number of unsafe behaviors is far less than that of safe ones in a behavior dataset of coal mine. Serious imbalance has a negative impact on recognition efficiency and accuracy. To address the problem, the harmonic information gain ratio is defined by introducing the degree of imbalance into traditional information gain, and the corresponding feature selection method is presented. By integrating it into Underbagging, a novel ensemble recognition based on the harmonic information gain ratio for unsafe behaviors is presented, with the purpose of avoiding information loss caused by feature reduction and guaranteeing recognition accuracy. Based on a sub-dataset obtained by undersampling, the optimal features subset is selected by the proposed feature selection method, and employed to train a base classifier built by support vector machine. The weighted sum of all base classifiers output forms final recognition result. Each weight is calculated from the corresponding harmonic information gain ratio. Experimental results on UCI dataset and a behavior dataset for a particular coal mine indicate that the proposed ensemble recognition method outperforms the others, especially for a dataset with high imbalance ratio.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dan, X., Yong, D., Junhong, J.: Research on driver behavior recognition method based on convolutional neural network. China Saf. Sci. J. 29(10), 12–17 (2019)
Guo, Y.N., Cheng, J., Luo, S., Gong, D., Xue, Y.: Robust dynamic multi-objective vehicle routing optimization method. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(6), 1891–1903 (2018). https://doi.org/10.1109/TCBB.2017.2685320
Guo, Y.N., Zhang, X., Gong, D.W., Zhang, Z., Yang, J.J.: Novel interactive preference-based multi-objective evolutionary optimization for bolt supporting networks. IEEE Trans. Evol. Comput. 24(4), 750–764 (2020)
Guo, Y., Yang, H., Chen, M., Cheng, J., Gong, D.: Ensemble prediction-based dynamic robust multi-objective optimization methods. Swarm Evol. Comput. 48, 156–171 (2019)
Hamdani, M., et al.: Class association and attribute relevancy based imputation algorithm to reduce twitter data for optimal sentiment analysis. IEEE Access 7, 136535–136544 (2019). https://doi.org/10.1109/ACCESS.2019.2942112
Jian, W., Wanjun, Y.: Hadoop-based behavior safety management model for methane gas. Mod. Electron. Tech. 42(21), 154–162 (2019)
Jianbao, Z., Zhilong, X., Yuwei, S., Qingshan, M.: Detection of dangerous behaviors in power stations based on open pose multi-person attitude recognition. Autom. Instrum. 35(02), 47–51 (2020)
Jiangshi, Z., Gui, F., Qiming, G., Liujun, H., Jiguo, W.: The pre-controlling measures on unsafe behavior. J. China Coal Soc. 037(A02), 373–377 (2012)
Kesuma Dinata, R., Novriando, H., Hasdyna, N., Retno, S.: Reduksi atribut menggunakan information gain untuk optimasi cluster algoritma k-means. Jurnal Edukasi dan Penelitian Informatika (JEPIN) 6, 48–53 (2020). https://doi.org/10.26418/jp.v6i1.37606
Paramitha, A., Indriati, Arum Sari, Y.: Analisis sentimen terhadap ulasan pengguna MRT jakarta menggunakan information gain dan modified k-nearest neighbor. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer 4, 1125–1132 (2020)
Wanzeng, K., Weicheng, L., Fabio, B., Sanqing, H., Gianluca, B.: Investigating driver fatigue versus alertness using the granger causality network. Sensors (Basel, Switzerland) 15(8), 19181–19198 (2015)
Yu, W.: Analysis on the improvement of unsafe behavior management. Shaanxi Coal S2, 121–123 (2020)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant 61973305, 61573361, Six Talent Peak Project in Jiangsu Province under Grant 2017-DZXX-046, Natural Science Foundation of Liaoning Province for the State Key Laboratory of Robotics under Grant 2020-KF-22-02.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Cheng, J., Jiao, B., Guo, Y., Wang, S. (2021). Ensemble Recognition Based on the Harmonic Information Gain Ratio for Unsafe Behaviors in Coal Mines. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-78811-7_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78810-0
Online ISBN: 978-3-030-78811-7
eBook Packages: Computer ScienceComputer Science (R0)