Recent publications
Article
He, Y, Wang, Y, Wang, S & Yao, X 2022, 'A cooperative ensemble method for multistep wind speed probabilistic forecasting', Chaos, Solitons and Fractals, vol. 162, 112416. https://doi.org/10.1016/j.chaos.2022.112416
He, Y, Cao, C, Wang, S & Fu, H 2022, 'Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems', Applied Energy, vol. 322, 119507. https://doi.org/10.1016/j.apenergy.2022.119507
Wang, X, Wang, H, Wang, S, Liu, Y, Yu, W, Wang, J, Xu, Q & Li, X 2022, 'Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model', Remote Sensing of the Environment, vol. 272, 112940. https://doi.org/10.1016/j.rse.2022.112940
Guo, Y, Jiao, L, Qu, R, Sun, Z, Wang, S, Wang, S & Liu, F 2021, 'Adaptive fuzzy learning superpixels representation for PolSAR image classification', IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3128908
He, Y, Li, H, Wang, S & Yao, X 2021, 'Uncertainty analysis of wind power probability density forecasting based on cubic spline interpolation and support vector quantile regression', Neurocomputing, vol. 430, pp. 121-137. https://doi.org/10.1016/j.neucom.2020.10.093
He, Y, Qin, Y, Wang, S, Wang, X & Wang, C 2019, 'Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network', Applied Energy, vol. 233-234, pp. 565-575. https://doi.org/10.1016/j.apenergy.2018.10.061
Zhang, H, Liu, W, Wang, S, Shan, J & Liu, Q 2019, 'Resample-based ensemble framework for drifting imbalanced data streams', IEEE Access, vol. 7, pp. 65103-65115. https://doi.org/10.1109/ACCESS.2019.2914725
Wang, S, Minku, LL & Yao, X 2018, 'A Systematic Study of Online Class Imbalance Learning With Concept Drift', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4802-4821. https://doi.org/10.1109/TNNLS.2017.2771290
Guo, Y, Jiao, L, Wang, S, Wang, S & Liu, F 2018, 'Fuzzy sparse autoencoder framework for single image per person face recognition', IEEE Transactions on Cybernetics, vol. 48, no. 8, pp. 2402-2415. https://doi.org/10.1109/TCYB.2017.2739338
Guo, Y, Jiao, L, Wang, S, Wang, S, Liu, F & Hua, W 2018, 'Fuzzy superpixels for polarimetric SAR images classification', IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 2846-2860. https://doi.org/10.1109/TFUZZ.2018.2814591
He, Y, Liu, R, Li, H, Wang, S & Lu, X 2017, 'Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory', Applied Energy, vol. 185, pp. 254-266. https://doi.org/10.1016/j.apenergy.2016.10.079
Conference contribution
Wang, Z & Wang, S 2023, Online automated machine learning for class imbalanced data streams. in 2023 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN), IEEE, International Joint Conference on Neural Networks, Queensland, Australia, 18/06/23.
Xiao, C & Wang, S 2022, An experimental study of class imbalance in federated learning. in 2021 IEEE Symposium Series on Computational Intelligence (SSCI)., 9660072, IEEE Symposium Series on Computational Intelligence, Institute of Electrical and Electronics Engineers (IEEE), IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021), Orlando, Florida, United States, 5/12/21. https://doi.org/10.1109/SSCI50451.2021.9660072
Wang, S & Minku, L 2020, AUC estimation and concept drift detection for imbalanced data streams with multiple classes. in Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020., 9207377, Proceedings of International Joint Conference on Neural Networks, IEEE Computer Society Press, IEEE International Joint Conference on Neural Networks (IJCNN), 2020 , Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/IJCNN48605.2020.9207377
Li, K, Xiang, Z, Chen, T, Wang, S & Tan, KC 2019, Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study. in 42nd International Conference on Software Engineering (ICSE 2020). Association for Computing Machinery (ACM), 42nd International Conference on Software Engineering (ICSE 2020), Seoul, Korea, Republic of, 23/05/20. https://doi.org/10.1145/3377811.3380360
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