An aggressive driving state recognition model using EEG based on stacking ensemble learning

References

• Agarwal, S., & Chowdary, C. R. (2020). A-stacking and a-bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection. Expert Systems with Applications, 146, 113160. doi:10.1016/j.eswa.2019.113160
   Web of Science ®Google Scholar
• Altameem, A., Kumar, A., Poonia, R. C., Kumar, S., & Saudagar, A. K. J. (2021). Early identification and detection of driver drowsiness by hybrid machine learning. IEEE Access. 9, 162805–162819. doi:10.1109/ACCESS.2021.3131601
   Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. doi:10.1007/BF00058655
   Web of Science ®Google Scholar
• Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. doi:10.1214/ss/1009213726
   Web of Science ®Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeye, W. P. (2002). Smote: Synthetic minority over-sampling techniq. Journal of Artificial Intelligence Research, 16, 321–357. doi:10.1613/jair.953
   Web of Science ®Google Scholar
• Cheng, L., Lai, X., Chen, X., Yang, S., Vos, J. D., & Witlox, F. (2020). Applying an ensemble based model to travel choice behavior in travel demand forecasting under uncertainties. Transportation Letters, 12(6), 375–385. doi:10.1080/19427867.2019.1603188
   Web of Science ®Google Scholar
• Džeroski, S., & Ženko, B. (2004). Is combining classifiers with stacking better. Machine Learning, 54(3), 255–273. doi:10.1023/B:MACH.0000015881.36452.6e
   Web of Science ®Google Scholar
• Fu, X., Meng, H., Wang, X., Yang, H., & Wang, J. (2022). A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data. PloS One, 17(1), e0263030. doi:10.1371/journal.pone.0263030
   PubMed Web of Science ®Google Scholar
• Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. doi:10.1007/s10994-006-6226-1
   Web of Science ®Google Scholar
• Hadi, W., El-Khalili, N., AlNashashibi, M., Issa, G., & AlBanna, A. A. (2019). Application of data mining algorithms for improving stress prediction of automobile drivers: A case study in Jordan. Computers in Biology and Medicine, 114, 103474. doi:10.1016/j.compbiomed.2019.103474
   PubMed Web of Science ®Google Scholar
• Halim, Z., & Rehan, M. (2020). On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning. Information Fusion, 53, 66–79. doi:10.1016/j.inffus.2019.06.006
   Web of Science ®Google Scholar
• Jin, L., Hua, Q., Zhang, S., & Guo, B. (2022). Stacking‐based ensemble learning method for cognitive distraction state recognition for drivers in traditional and connected environments. IET Intelligent Transport Systems, 16(1), 114–132. doi:10.1049/itr2.12133
   Web of Science ®Google Scholar
• Lahmyed, R., Ansari, M. E., & Kerkaou, Z. (2022). Automatic road sign detection and recognition based on neural network. Soft Computing, 26(4), 1743–1764. doi:10.1007/s00500-021-06726-w
   Web of Science ®Google Scholar
• Liu, L., Ji, Y., Gao, Y., Ping, Z., Kuang, L., Li, T., & Xu, W. (2021). A novel fatigue driving state recognition and warning method based on EEG and EOG signals. Journal of Healthcare Engineering, 2021, 7799793. doi:10.1155/2021/7799793
   PubMed Web of Science ®Google Scholar
• Liu, Y., Wang, J., Zhao, P., Qin, D., & Chen, Z. (2019). Research on classification and recognition of driving styles based on feature engineering. IEEE Access. 7, 89245–89255. doi:10.1109/ACCESS.2019.2926593
   Web of Science ®Google Scholar
• Maldonado, S., López, J., & Vairetti, C. (2019). An alternative smote oversampling strategy for high-dimensional datasets. Applied Soft Computing, 76, 380–389. doi:10.1016/j.asoc.2018.12.024
   Web of Science ®Google Scholar
• Maldonado, S., Vairetti, C., Fernandez, A., & Herrera, F. (2022). Fw-smote: A feature-weighted oversampling approach for imbalanced classification. Pattern Recognition, 124, 108511. doi:10.1016/j.patcog.2021.108511
   Web of Science ®Google Scholar
• Ma, Y., Gu, G., Yin, B., Qi, S., Chen, K., & Chan, C. (2022). Support vector machines for the identification of real-time driving distraction using in-vehicle information systems. Journal of Transportation Safety & Security, 14(2), 232–255. doi:10.1080/19439962.2020.1774019
   Web of Science ®Google Scholar
• Pavlyuk, D. (2020). Towards ensemble learning of traffic flows’ spatiotemporal structure. Transportation Research Procedia, 47, 361–368. doi:10.1016/j.trpro.2020.03.110
   Google Scholar
• Romera, E., Bergasa, L. M., & Arroyo, R. (2016). Need data for driver behaviour analysis? Presenting the public uah-driveset [Paper presentation]. 2016 IEEE 19th International Conference on Intelligent Transportation Systems.
   Google Scholar
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating. Nature, 323(6088), 533–536. doi:10.1038/323533a0
   Web of Science ®Google Scholar
• Saiprasert, C., Pholprasit, T., & Thajchayapong, S. (2017). Detection of driving events using sensory data on smartphone. International Journal of Intelligent Transportation Systems Research, 15(1), 17–28. doi:10.1007/s13177-015-0116-5
   Web of Science ®Google Scholar
• Schapire, R. (1990). Strength of weak learnability. Machine Learning, 5(2), 197–227. doi:10.1007/BF00116037
   Web of Science ®Google Scholar
• Sun, C., Wu, C., Chu, D., Lu, Z., Tan, J., & Wang, J. (2018). A recognition model of driving risk based on belief rule-base methodology. International Journal of Pattern Recognition and Artificial Intelligence, 32(11), 1850037. doi:10.1142/S0218001418500374
   Web of Science ®Google Scholar
• Wang, K., & Lu, J. J. (2021). A two-layer risky driver recognition model with context awareness. IEEE Access, 9, 138483–138495. doi:10.1109/ACCESS.2021.3116996
   Web of Science ®Google Scholar
• Wang, K., Xue, Q., Xing, Y., & Li, C. (2020). Improve aggressive driver recognition using collision surrogate measurement and imbalanced class boosting. International Journal of Environmental Research and Public Health. 17(7), 2375.
   Web of Science ®Google Scholar
• Winkler, R. L. (1989). Combining forecasts a philosophical basis and some current issues. International Journal of Forecasting, 5(4), 605–609. doi:10.1016/0169-2070(89)90018-6
   Web of Science ®Google Scholar
• Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259. doi:10.1016/S0893-6080(05)80023-1
   Web of Science ®Google Scholar
• Wu, H., & Levinson, D. (2021). The ensemble approach to forecasting: A review and synthesis. Transportation Research Part C: Emerging Technologies, 132, 103357. doi:10.1016/j.trc.2021.103357
   Web of Science ®Google Scholar
• Yang, L., Guan, W., Ma, R., & Li, X. (2019). Comparison among driving state prediction models for car-following condition based on EEG and driving features. Accident; Analysis and Prevention, 133, 105296. doi:10.1016/j.aap.2019.105296
   PubMed Web of Science ®Google Scholar
• Yang, L., Ma, R., Zhang, H. M., Guan, W., & Jiang, S. (2018). Driving behavior recognition using EEG data from a simulated car-following experiment. Accident; Analysis and Prevention, 116, 30–40. doi:10.1016/j.aap.2017.11.010
   PubMed Web of Science ®Google Scholar
• Ye, B., Qiu, T., Bai, X., & Liu, P. (2018). Research on recognition method of driving fatigue state based on sample entropy and kernel principal component analysis. Entropy (Basel), 20(9), 701. doi:10.3390/e20090701
   PubMed Web of Science ®Google Scholar
• Yi, D., Su, J., Liu, C., Quddus, M., & Chen, W.-H. (2019). A machine learning based personalized system for driving state recognition. Transportation Research Part C: Emerging Technologies, 105, 241–261. doi:10.1016/j.trc.2019.05.042
   Web of Science ®Google Scholar
• Yu, B., Lam, W. H. K., & Tam, M. L. (2011). Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C: Emerging Technologies, 19(6), 1157–1170. doi:10.1016/j.trc.2011.01.003
   Web of Science ®Google Scholar
• Yuan, F., & Cheu, R. L. (2003). Incident detection using support vector machines. Transportation Research Part C: Emerging Technologies, 11(3-4), 309–328. doi:10.1016/S0968-090X(03)00020-2
   Web of Science ®Google Scholar
• Zhang, G., Cai, Y., Jiang, X., Fan, Y., Zhou, Y., & Qian, J. (2023). An ensemble machine learning method for crash responsibility assignment in quasi-induced exposure theory. Journal of Transportation Safety & Security, 15(1), 24–42. doi:10.1080/19439962.2022.2026543
   Web of Science ®Google Scholar
• Zhang, G., Liang, Q., Jiang, X., Yang, S., Fan, Y., Qiu, X., Yao, T., & Mao, C. (2022). Examining the causal effects of distracted driving on crash injury severities. Journal of Transportation Safety & Security, 14(8), 1358–1377. doi:10.1080/19439962.2021.1923102
   Web of Science ®Google Scholar
• Zhang, J., Wang, T., Ng, W. W. Y., & Pedrycz, W. (2022). Ensembling perturbation-based oversamplers for imbalanced datasets. Neurocomputing, 479, 1–11. doi:10.1016/j.neucom.2022.01.049
   Web of Science ®Google Scholar
• Zhao, H., Liu, J., Shen, Z., & Yan, J. (2022). SCC-MPGCN: Self-attention coherence clustering based on multi-pooling graph convolutional network for EEG emotion recognition. Journal of Neural Engineering, 19(2), 026051. doi:10.1088/1741-2552/ac6294
   Web of Science ®Google Scholar