The IEEE CIS Open Book I contributed to has been released, and we now also have a webpage for the book: https://ieee-cis.github.io/IEEE-CIS-Open-Access-Book-Volume-1/
Title: Introduction to Computational Intelligence
Title: Introduction to Computational Intelligence
Journal Publications
[1] Shuo Wang and Xin Yao, "Relationships Between Diversity of Classification Ensembles and Single-Class Performance Measures", IEEE Transactions on Knowledge and Data Engineering, 25(1):206-219, January 2013.
Available [here] or [online].
Code is available [here] in Java (Weka 3.4 required).
[2] Shuo Wang and Xin Yao, "Multi-Class Imbalance Problems: Analysis and Potential Solutions", IEEE Transactions on Systems, Man and Cybernetics, PartB: Cybernetics, 42(4):1119-1130, August 2012.
Available [here] or [online].
Code is available [here] in Java (weka 3.7 required).
[3] Shuo Wang and Xin Yao, "Using Class Imbalance Learning for Software Defect Prediction", IEEE Transactions on Reliability, 62(2):434-443, 2012.
PDF is available [here].
Code is available [here] in Java (weka 3.7 required).
[4] Shuo Wang, Leandro L.Minku and Xin Yao, "Online Class Imbalance Learning and Its Applications in Fault Detection", Special Issue of International Journal of Computational Intelligence and Applications, 12(4):1340001(1-19),2013. [pdf]
[5] Ronghua Shang, Yuying Wang, Jia Wang, Licheng Jiao, Shuo Wang and Liping Qi, "A Multi-population Cooperative Coevolutionary Algorithm for Multi-objective Capacitated Arc Routing Problem", Information Sciences, March 2014.
[6] Shuo Wang, Leandro L.Minku and Xin Yao, "Resampling-Based Ensemble Methods for Online Class Imbalance Learning", IEEE Transactions on Knowledge and Data Engineering, 27(5):1356-1368, 2015. [pdf]
Code is available [here] in Java (Weka 3.7 required).
[7] Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu, Kaixuan Rong and Tao Xiong, "A novel dynamic rough subspace based selective ensemble", Pattern Recognition, DOI: 10.1016/j.patcog.2014.11.001. [pdf]
[8] Y. Sun, K. Tang, L.L.Minku, S. Wang and X. Yao, "Online Ensemble Learning of Data Streams with Gradually Evolved Classes", IEEE Transactions on Knowledge and Data Engineering, 28(6):1532 - 1545, 2016. [pdf]
[9] Y. He, R. Liu, H. Li, S. Wang and X. Lu, "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory", Applied Energy 185, pp. 254-266, 2017. [pdf]
[10] Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang and Fang Liu, "Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition", IEEE Transactions on Cybernetics, vol. 48, no. 8, pages 2402 - 2415, August 2017. [pdf]
[11] Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu and Wenqiang Hua, "Fuzzy-Superpixels for Polarimetric SAR Images Classification", IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pages 2846 - 2860, 2018. [pdf]
[12] Shuo Wang, Leandro L.Minku and Xin Yao, "A Systematic Study of Online Class Imbalance Learning With Concept Drift", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pages 4802 - 4821, 2018. [pdf]
[13] Yaoyao He, Yang Qin, Shuo Wang, Xu Wang and Chao Wang, "Electricity Consumption Probability Density Forecasting Method based on LASSO-Quantile Regression Neural Network", Applied Energy, Volumes 233–234, Pages 565-575,
2018. [pdf]
[14] Hang Zhang, Weike Liu, Shuo Wang, Jicheng Shan, and Qingbao Liu, "Resample-based Ensemble Framework for Drifting Imbalanced Data Streams", IEEE Access, vol. 7, pages 65103 - 65115, 2019. [pdf]
[15] Yaoyao He, Haiyan Li, Shuo Wang and Xin Yao, "Uncertainty Analysis of Wind Power Probability Density Forecasting based on Cubic Spline Interpolation and Support Vector Quantile Regression", Neurocomputing, 2020, Accepted on 26th of Oct. [pdf][16] Yuwei Guo, Licheng Jiao, Rong Qu, Zhuangzhuang Sun, Shuang Wang, Shuo Wang and Fang Liu "Adaptive Fuzzy Learning Superpixels Representation for PolSAR Image Classification", IEEE Transactions on Geoscience and Remote Sensing, 2021, DOI: 10.1109/TGRS.2021.3128908 (Accepted October 19, 2021). [pdf]
[17] Xudong Zhang, Haoyu Wang, Shuo Wang, Yanliang Liu, Weidong Yu, Jing Wang, Qing Xu and Xiaofeng Li, "Oceanic Internal Wave Amplitude Retrieval from Satellite Images Based on a Data-Driven Transfer Learning Model", Remote Sensing of Environment, vol. 272, 2022, DOI: https://doi.org/10.1016/j.rse.2022.112940 (Accepted February 1, 2022). [pdf]
[18] Yaoyao He, Chaojin Cao, Shuo Wang and Hong Fu, "Nonparametric Probabilistic Load Forecasting based on Quantile Combination in Electrical Power Systems", Applied Energy, 2022 (Accepted June 17, 2022). [link]
[19] Lei Han, Handing Wang and Shuo Wang, "A Surrogate-Assisted Evolutionary Algorithm for Space Component Thermal Layout Optimization", Space: Science & Technology, vol. 2022, Article ID 9856362, 2022. [pdf]
[20] Yaoyao He, Yun Wang, Shuo Wang and Xin Yao, "A Cooperative Ensemble Method for Multistep Wind Speed Probabilistic Forecasting", Chaos, Solitons & Fractals, 2022 (Accepted July 2, 2022).
[21] Xiaomei Jiang, Shuo Wang, Wenjian Liu and Yun Yang, "Prediction of traditional Chinese medicine prescriptions based on multi-label resampling", Journal of Electronic Business & Digital Economics, pp. 213-227, vol. 2, no. 2, 2023 (article no. JEBDE 04-2023-0009) [pdf].
[22] Xiaoning Shen, Hongli Pan, Zhongpei Ge, Wenyan Chen, Liyan Song and Shuo Wang, "Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization," in Complex System Modeling and Simulation, vol. 3, no. 3, pp. 202-219, September 2023, doi: 10.23919/CSMS.2023.0008.
[23] Yaoyao He, Jianhua Zhu and Shuo Wang, "A Novel Neural Network-based Multi-objective Evolution Lower Upper Bound Estimation Method for Electricity Load Interval Forecast", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024.
[24] Yaoyao He, Jingjing Zhou, Chaojin Cao, Shuo Wang and Hong Fu, "Detection of Electricity Theft based on Minimal Gated Memory Network Combined Adaptive Synthesis Sampling and Decision Tree", Sustainable Energy, Grids and Networks, 2024.
[25] Nutta Skanupong, Yongsheng Xu, Lejiang Yu, Zhang Wan, Shuo Wang, "The Convolutional Neural Network for Pacific Decadal Oscillation Forecast", Environmental Research Letters, 2024 (accepted on 21st Oct).
[26] Yuwei Guo, Wenhao Zhang, Yupeng Gao, Licheng Jiao, Shuo Wang, Jiabo Du and Fang Liu, "Revisiting non-learned operators based deep learning for image classification: a lightweight directional-aware network", Artificial Intelligence Review, Springer, 2025 [pdf].
[27] Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang and Fang Liu, "Visible-infrared person re-identification with region-based augmentation and cross modality attention", Scientific Reports, 15: 18225, Nature, 2025 [online].
[28] Huanbo Lyu, Daniel Herring, Hongkuan Chen, Shiqiao Zhou, Jianyu Zhang, Lingfeng Wang, Zheming Zuo, James Andrews, Michal Kocvara, Fabian Spill, Jelena Ninic and Shuo Wang, "A Data-Driven Multi-Objective Optimisation Framework for Energy Efficiency and Thermal Comfort in Flexible Building Spaces", Energy & Buildings, 2025 (accepted on 1st of July). [link]
[29] Zhaoyang Wang, Shuo Wang, Damien Ernst and Chenguang Xiao, "Automated Class Imbalance Learning via Few-shot Multi-Objective Bayesian Optimization with Deep Kernel Gaussian Processes", IEEE Access, 2025 (accepted on 14th of July). [link]
[30] Chaojin Cao, Yaoyao He, Yue Zhou and Shuo Wang, "An Online Probabilistic Combination Framework for Power Load Forecasting Under Concept-Drifting Scenarios", Applied Energy, 2025 (accepted on 21st of July). [link]
[31] Xiaoting Chen, Shuo Wang, Tengsen Zhang, Guanhui Yang and Yun Yang, "Virtual Concept Drift Detection and Adaptation in Federated Data Stream Learning", International Journal of Data Science and Analytics, Springer Nature, 2025 [link].
[32] Sean Groom, Francisco Belo, Axl Rice, Liam Anderson and Shuo Wang, "A Machine Learning Framework for Off-Ball Defensive Evaluation Applied to Corner Kicks", npj Artificial Intelligence, Springer Nature, 2026 (Accepted June 29th).
Available [here] or [online].
Code is available [here] in Java (Weka 3.4 required).
[2] Shuo Wang and Xin Yao, "Multi-Class Imbalance Problems: Analysis and Potential Solutions", IEEE Transactions on Systems, Man and Cybernetics, PartB: Cybernetics, 42(4):1119-1130, August 2012.
Available [here] or [online].
Code is available [here] in Java (weka 3.7 required).
[3] Shuo Wang and Xin Yao, "Using Class Imbalance Learning for Software Defect Prediction", IEEE Transactions on Reliability, 62(2):434-443, 2012.
PDF is available [here].
Code is available [here] in Java (weka 3.7 required).
[4] Shuo Wang, Leandro L.Minku and Xin Yao, "Online Class Imbalance Learning and Its Applications in Fault Detection", Special Issue of International Journal of Computational Intelligence and Applications, 12(4):1340001(1-19),2013. [pdf]
[5] Ronghua Shang, Yuying Wang, Jia Wang, Licheng Jiao, Shuo Wang and Liping Qi, "A Multi-population Cooperative Coevolutionary Algorithm for Multi-objective Capacitated Arc Routing Problem", Information Sciences, March 2014.
[6] Shuo Wang, Leandro L.Minku and Xin Yao, "Resampling-Based Ensemble Methods for Online Class Imbalance Learning", IEEE Transactions on Knowledge and Data Engineering, 27(5):1356-1368, 2015. [pdf]
Code is available [here] in Java (Weka 3.7 required).
[7] Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu, Kaixuan Rong and Tao Xiong, "A novel dynamic rough subspace based selective ensemble", Pattern Recognition, DOI: 10.1016/j.patcog.2014.11.001. [pdf]
[8] Y. Sun, K. Tang, L.L.Minku, S. Wang and X. Yao, "Online Ensemble Learning of Data Streams with Gradually Evolved Classes", IEEE Transactions on Knowledge and Data Engineering, 28(6):1532 - 1545, 2016. [pdf]
[9] Y. He, R. Liu, H. Li, S. Wang and X. Lu, "Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory", Applied Energy 185, pp. 254-266, 2017. [pdf]
[10] Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang and Fang Liu, "Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition", IEEE Transactions on Cybernetics, vol. 48, no. 8, pages 2402 - 2415, August 2017. [pdf]
[11] Yuwei Guo, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu and Wenqiang Hua, "Fuzzy-Superpixels for Polarimetric SAR Images Classification", IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pages 2846 - 2860, 2018. [pdf]
[12] Shuo Wang, Leandro L.Minku and Xin Yao, "A Systematic Study of Online Class Imbalance Learning With Concept Drift", IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pages 4802 - 4821, 2018. [pdf]
[13] Yaoyao He, Yang Qin, Shuo Wang, Xu Wang and Chao Wang, "Electricity Consumption Probability Density Forecasting Method based on LASSO-Quantile Regression Neural Network", Applied Energy, Volumes 233–234, Pages 565-575,
2018. [pdf]
[14] Hang Zhang, Weike Liu, Shuo Wang, Jicheng Shan, and Qingbao Liu, "Resample-based Ensemble Framework for Drifting Imbalanced Data Streams", IEEE Access, vol. 7, pages 65103 - 65115, 2019. [pdf]
[15] Yaoyao He, Haiyan Li, Shuo Wang and Xin Yao, "Uncertainty Analysis of Wind Power Probability Density Forecasting based on Cubic Spline Interpolation and Support Vector Quantile Regression", Neurocomputing, 2020, Accepted on 26th of Oct. [pdf][16] Yuwei Guo, Licheng Jiao, Rong Qu, Zhuangzhuang Sun, Shuang Wang, Shuo Wang and Fang Liu "Adaptive Fuzzy Learning Superpixels Representation for PolSAR Image Classification", IEEE Transactions on Geoscience and Remote Sensing, 2021, DOI: 10.1109/TGRS.2021.3128908 (Accepted October 19, 2021). [pdf]
[17] Xudong Zhang, Haoyu Wang, Shuo Wang, Yanliang Liu, Weidong Yu, Jing Wang, Qing Xu and Xiaofeng Li, "Oceanic Internal Wave Amplitude Retrieval from Satellite Images Based on a Data-Driven Transfer Learning Model", Remote Sensing of Environment, vol. 272, 2022, DOI: https://doi.org/10.1016/j.rse.2022.112940 (Accepted February 1, 2022). [pdf]
[18] Yaoyao He, Chaojin Cao, Shuo Wang and Hong Fu, "Nonparametric Probabilistic Load Forecasting based on Quantile Combination in Electrical Power Systems", Applied Energy, 2022 (Accepted June 17, 2022). [link]
[19] Lei Han, Handing Wang and Shuo Wang, "A Surrogate-Assisted Evolutionary Algorithm for Space Component Thermal Layout Optimization", Space: Science & Technology, vol. 2022, Article ID 9856362, 2022. [pdf]
[20] Yaoyao He, Yun Wang, Shuo Wang and Xin Yao, "A Cooperative Ensemble Method for Multistep Wind Speed Probabilistic Forecasting", Chaos, Solitons & Fractals, 2022 (Accepted July 2, 2022).
[21] Xiaomei Jiang, Shuo Wang, Wenjian Liu and Yun Yang, "Prediction of traditional Chinese medicine prescriptions based on multi-label resampling", Journal of Electronic Business & Digital Economics, pp. 213-227, vol. 2, no. 2, 2023 (article no. JEBDE 04-2023-0009) [pdf].
[22] Xiaoning Shen, Hongli Pan, Zhongpei Ge, Wenyan Chen, Liyan Song and Shuo Wang, "Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization," in Complex System Modeling and Simulation, vol. 3, no. 3, pp. 202-219, September 2023, doi: 10.23919/CSMS.2023.0008.
[23] Yaoyao He, Jianhua Zhu and Shuo Wang, "A Novel Neural Network-based Multi-objective Evolution Lower Upper Bound Estimation Method for Electricity Load Interval Forecast", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024.
[24] Yaoyao He, Jingjing Zhou, Chaojin Cao, Shuo Wang and Hong Fu, "Detection of Electricity Theft based on Minimal Gated Memory Network Combined Adaptive Synthesis Sampling and Decision Tree", Sustainable Energy, Grids and Networks, 2024.
[25] Nutta Skanupong, Yongsheng Xu, Lejiang Yu, Zhang Wan, Shuo Wang, "The Convolutional Neural Network for Pacific Decadal Oscillation Forecast", Environmental Research Letters, 2024 (accepted on 21st Oct).
[26] Yuwei Guo, Wenhao Zhang, Yupeng Gao, Licheng Jiao, Shuo Wang, Jiabo Du and Fang Liu, "Revisiting non-learned operators based deep learning for image classification: a lightweight directional-aware network", Artificial Intelligence Review, Springer, 2025 [pdf].
[27] Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang and Fang Liu, "Visible-infrared person re-identification with region-based augmentation and cross modality attention", Scientific Reports, 15: 18225, Nature, 2025 [online].
[28] Huanbo Lyu, Daniel Herring, Hongkuan Chen, Shiqiao Zhou, Jianyu Zhang, Lingfeng Wang, Zheming Zuo, James Andrews, Michal Kocvara, Fabian Spill, Jelena Ninic and Shuo Wang, "A Data-Driven Multi-Objective Optimisation Framework for Energy Efficiency and Thermal Comfort in Flexible Building Spaces", Energy & Buildings, 2025 (accepted on 1st of July). [link]
[29] Zhaoyang Wang, Shuo Wang, Damien Ernst and Chenguang Xiao, "Automated Class Imbalance Learning via Few-shot Multi-Objective Bayesian Optimization with Deep Kernel Gaussian Processes", IEEE Access, 2025 (accepted on 14th of July). [link]
[30] Chaojin Cao, Yaoyao He, Yue Zhou and Shuo Wang, "An Online Probabilistic Combination Framework for Power Load Forecasting Under Concept-Drifting Scenarios", Applied Energy, 2025 (accepted on 21st of July). [link]
[31] Xiaoting Chen, Shuo Wang, Tengsen Zhang, Guanhui Yang and Yun Yang, "Virtual Concept Drift Detection and Adaptation in Federated Data Stream Learning", International Journal of Data Science and Analytics, Springer Nature, 2025 [link].
[32] Sean Groom, Francisco Belo, Axl Rice, Liam Anderson and Shuo Wang, "A Machine Learning Framework for Off-Ball Defensive Evaluation Applied to Corner Kicks", npj Artificial Intelligence, Springer Nature, 2026 (Accepted June 29th).
Conference Publications
[1] S.Wang and X.Yao (12/2008). Diversity Analysis on Imbalanced Data Sets by Using Ensemble Models. IEEE Symposium on Computational Intelligence and Data Mining 2009, Nashville, TN, USA. Pages 324-331, 2009. [pdf]
[2] S.Wang and K.Tang and X.Yao (01/2009). Diversity Exploration and Negative Correlation Learning on Imbalanced Data Sets. International Joint Conference on Neural Networks 2009, Atlanta, Georgia, USA. Pages 3259-3266, 2009. (Travel Grant Awarded) [pdf]
[3] S.Wang and X.Yao (07/2009). Theoretical Study of the Relationship Between Diversity and Single-Class Measures for Class Imbalance Learning. IEEE International Conference on Data Mining Workshops 2009, Miami, Florida, USA. Pages 76-81, 2009. [pdf]
[4] S.Wang and H.Chen and X.Yao (02/2010). Negative Correlation Learning for Classification Ensembles. International Joint Conference on Neural Networks 2010, Barcelona, Spain. Pages 2893-2900, 2010. (Travel Grant Awarded)[pdf]
[5] S.Wang and X.Yao (07/2010). The Effectiveness of A New Negative Correlation Learning Algorithm for Classification Ensembles. IEEE International Conference on Data Mining Workshops 2010, Sydney, Australia. Pages 1013-1020, 2010. [pdf]
[6] S.Wang, L.L.Minku and X.Yao (01/2013). A Learning Framework for Online Class Imbalance Learning. IEEE Symposium Series on Computational Intelligence (SSCI) 2013, Singapore. Pages 36-45, 2013. [pdf]
[7] S.Wang, L.L.Minku, D.Ghezzi, D.Caltabiano, P.Tino and X.Yao (04/2013). Concept Drift Detection for Online Class Imbalance Learning. In International Joint Conference on Neural Networks (IJCNN '13). 1-10, 2013. [pdf]
[8] S.Wang, L.L.Minku, and X.Yao. A Multi-Objective Ensemble Method for Online Class Imbalance Learning. In International Joint Conference on Neural Networks (IJCNN '14). Pages 3311-3318, 2014. [pdf]
[9] S.Wang, L.L.Minku, and X.Yao. Dealing with Multiple Classes in Online Class Imbalance Learning. In the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). Pages 2118-2124, 2016. [pdf]
Code is available [here] in Java (Weka 3.7 and moa201208 required).
[10] T. Chen, R. Bahsoon, S.Wang and X.Yao. To Adapt or Not to Adapt? Technical Debt and Learning Driven Self-Adaptation for Managing Runtime Performance. In the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018). Berlin, Germany, 2018. [pdf]
[11] Jorge Casillas, Shuo Wang, and Xin Yao. Concept Drift Detection in Histogram-Based Straightforward Data Stream Prediction. In the 6th International Workshop on Data Science and Big Data Analytics (DSBDA), in Conjunction with IEEE International Conference on Data Mining (ICDM 2018), Singapore, 2018. [pdf]
[12] Ke Li, Zilin Xiang, Tao Chen, Shuo Wang, and Kay Chen Tan. Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study. In the 42nd International Conference on Software Engineering (ICSE), South Korea, page 566-577, 2020. [pdf]
[13] Shuo Wang, and Leandro L. Minku. AUC Estimation and Concept Drift Detection for Imbalanced Data Streams with Multiple Classes. In the Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE world congress on computational Intelligence, Glasgow, UK, 2020. [pdf]
[14] Chenguang Xiao and Shuo Wang. An Experimental Study of Class Imbalance in Federated Learning. In IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, USA, 2021. [pdf]
[15] Xudong Zhang, Haoyu Wang, Shuo Wang, Yanliang Liu, Weidong Yu and Xiaofeng Li. A Machine-learning-based Model to Inverse Internal Solitary Wave Amplitude from Satellite Image. Photonics & Electromagnetics Research Symposium (PIERS), Hangzhou, China, 2022. [pdf]
[16] Zhaoyang Wang and Shuo Wang. Online Automated Machine Learning for Class Imbalanced Data Streams. In the Proceedings of the International Joint Conference on Neural Networks (IJCNN), Australia, 2023. [link]
[17] Chenguang Xiao and Shuo Wang. Triplets Oversampling for Class Imbalanced Federated Datasets. In the Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Italy, 2023 (accepted on 6th of June). [link]
[18] Guanhui Yang, Xiaoting Chen, Tengsen Zhang, Shuo Wang and Yun Yang. An Impact Study of Concept Drift in Federated Learning, In the Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM), Shanghai, China, 2023.
[19] Huanbo Lyu, Daniel Herring, Lingfeng Wang, Jelena Ninic, James Andrews, Miqing Li, Michal Kocvara, Fabian Spill and Shuo Wang. Multi-Objective Optimization for Flexible Building Space Usage. In the Proceedings of IEEE Conference on Artificial Intelligence (CAI), Singapore, 2024.
[20] Sean Groom, Dan Morris, Liam Anderson and Shuo Wang. Modeling Defensive Dynamics in Football: A Hidden Markov Model-Based Approach for Man-Marking and Zonal Defending Corner Analysis. The 2nd International Workshop on Intelligent Technologies for Precision Sports Science (IT4PSS) in Conjunction with the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), South Korea, 2024. (Accepted 5th of June, 2024, in collaboration with Nottingham Forest Football Club).
[21] Zhaoyang Wang and Shuo Wang. Online Automated Imbalanced Learning via Adaptive Thompson Sampling. The 27th International Conference on Pattern Recognition (ICPR'24), India. (Accepted 17th of August, 2024)
[22] Zhaoyang Wang and Shuo Wang. Zero-shot Automated Class Imbalanced Learning. The 27th International Conference on Pattern Recognition (ICPR'24), India. (Accepted 17th of August, 2024)
[23] Guanhui Yang, Xiaoting Chen, Tengsen Zhang and Shuo Wang. A Multi-Model Approach for Handling Concept Drifting Data in Federated Learning. Workshop on Distributed Machine Learning and Unlearning for Sensor-Cloud Systems (DLS2) in conjunction with the 20th International Conference on Mobility, Sensing and Networking (MSN2024), China, 2024.
[24] Xiaoting Chen, Shuo Wang, Tengsen Zhang and Guanhui Yang. A Study of Virtual Concept Drift in Federated Data Stream Learning. The 2nd Workshop on Distributed and Integrated Communication, Sensing, and Computing in Space-Air-Ground Integrated Networks for 6G (DICSC) in conjunction with the 20th International Conference on Mobility, Sensing and Networking (MSN2024), China, 2024.
[25] Tengsen Zhang, Xiaoting Chen, Guanhui Yang and Shuo Wang. Overcoming Dynamic Class Imbalance in Federated Data Stream Learning. Workshop on Network Meets Intelligent Computations (NMIC) in conjunction with the 20th International Conference on Mobility, Sensing and Networking (MSN 2024), China, 2024.
[26] Liyan Yang, Shiqiao Zhou, Jingwei Cheng, Fu Zhang, Jicheng Wan, Shuo Wang and Mark Lee. DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation. In the 31st International Conference on Computational Linguistics, Abu Dhabi, UAE, 2024.
[27] Chenguang Xiao, Zheming Zuo, Shuo Wang. FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation. The 1st Workshop on Federated Learning for Unbounded and Intelligent Decentralization (FLUID) in conjunction with AAAI 2025.
[28] Zhaoyang Wang, Shuo Wang, Damien Ernst. Automated Class Imbalance Learning via Few-shot Bayesian Optimization with Meta-learned Deep Kernel Surrogates. The International Joint Conference on Neural Networks (IJCNN'25), Rome, Italy, 2025.
[29] Hao Li and Shuo Wang. A Two-Stage Multi-Source Transfer Learning Approach for Software Defect Prediction. The 23rd IEEE International Conference on Industrial Informatics (INDIN), Kunming, China, 2025.
[30] Xudong Guo and Shuo Wang. FedPAC: A Federated Semi-Supervised Learning Approach for Non-IID Data with Feature Shift. The IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, 2025.
[31] Shiqiao Zhou, Holger Schöner, Huanbo Lyu, Edouard Fouché and Shuo Wang. BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting. The 34th ACM International Conference on Information and Knowledge Management, Seoul, Korea, 2025.
[32] Hubo Yuan and Shuo Wang. SSDIR: A Novel Semi-Supervised Approach for Data Imbalanced Regression. The 14th International Conference on Computing and Pattern Recognition (ICCPR 2025), Beijing, China, 2025.
[33] Shiqiao Zhou, Holger Schöner, Zipeng Wu, Edouard Fouché, Iag Wilson and Shuo Wang. Stationarity-Aware Retrieval-Augmented Time Series Forecasting. The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'26), Jeju, Korea, 2026.
[34] Shiqiao Zhou, Zipeng Wu, Holger Schöner, Edouard Fouché, Iag Wilson and Shuo Wang. Semantics-enhanced retrieval-augmented time series forecasting. The ICML 2026 Workshop: Forecasting as a New Frontier of Intelligence, Seoul, Korea, 2026.
[35] Sean Groom, Michael Groom, Fancisco Belo, Axl Rice, Liam Anderson, Victor-Alexandru Darvariu and Shuo Wang. Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning. The 13th Workshop on the Machine Learning and Data Mining for Sports Analytics (MLSA), in conjunction with ECML/PKDD'26, Naples, Italy (Accepted on 16th of July), 2026.
[36] Bakhyt Bakiyev, Shuo Wang and Mubashir Ali. Model Comparison for Kazakh Semantic Reuse Detection Across Pairwise, News, and Long-Document Evaluation. The Conference and Labs of the Evaluation Forum (CLEF), Jena, Germany, 2026.
[2] S.Wang and K.Tang and X.Yao (01/2009). Diversity Exploration and Negative Correlation Learning on Imbalanced Data Sets. International Joint Conference on Neural Networks 2009, Atlanta, Georgia, USA. Pages 3259-3266, 2009. (Travel Grant Awarded) [pdf]
[3] S.Wang and X.Yao (07/2009). Theoretical Study of the Relationship Between Diversity and Single-Class Measures for Class Imbalance Learning. IEEE International Conference on Data Mining Workshops 2009, Miami, Florida, USA. Pages 76-81, 2009. [pdf]
[4] S.Wang and H.Chen and X.Yao (02/2010). Negative Correlation Learning for Classification Ensembles. International Joint Conference on Neural Networks 2010, Barcelona, Spain. Pages 2893-2900, 2010. (Travel Grant Awarded)[pdf]
[5] S.Wang and X.Yao (07/2010). The Effectiveness of A New Negative Correlation Learning Algorithm for Classification Ensembles. IEEE International Conference on Data Mining Workshops 2010, Sydney, Australia. Pages 1013-1020, 2010. [pdf]
[6] S.Wang, L.L.Minku and X.Yao (01/2013). A Learning Framework for Online Class Imbalance Learning. IEEE Symposium Series on Computational Intelligence (SSCI) 2013, Singapore. Pages 36-45, 2013. [pdf]
[7] S.Wang, L.L.Minku, D.Ghezzi, D.Caltabiano, P.Tino and X.Yao (04/2013). Concept Drift Detection for Online Class Imbalance Learning. In International Joint Conference on Neural Networks (IJCNN '13). 1-10, 2013. [pdf]
[8] S.Wang, L.L.Minku, and X.Yao. A Multi-Objective Ensemble Method for Online Class Imbalance Learning. In International Joint Conference on Neural Networks (IJCNN '14). Pages 3311-3318, 2014. [pdf]
[9] S.Wang, L.L.Minku, and X.Yao. Dealing with Multiple Classes in Online Class Imbalance Learning. In the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). Pages 2118-2124, 2016. [pdf]
Code is available [here] in Java (Weka 3.7 and moa201208 required).
[10] T. Chen, R. Bahsoon, S.Wang and X.Yao. To Adapt or Not to Adapt? Technical Debt and Learning Driven Self-Adaptation for Managing Runtime Performance. In the 9th ACM/SPEC International Conference on Performance Engineering (ICPE 2018). Berlin, Germany, 2018. [pdf]
[11] Jorge Casillas, Shuo Wang, and Xin Yao. Concept Drift Detection in Histogram-Based Straightforward Data Stream Prediction. In the 6th International Workshop on Data Science and Big Data Analytics (DSBDA), in Conjunction with IEEE International Conference on Data Mining (ICDM 2018), Singapore, 2018. [pdf]
[12] Ke Li, Zilin Xiang, Tao Chen, Shuo Wang, and Kay Chen Tan. Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study. In the 42nd International Conference on Software Engineering (ICSE), South Korea, page 566-577, 2020. [pdf]
[13] Shuo Wang, and Leandro L. Minku. AUC Estimation and Concept Drift Detection for Imbalanced Data Streams with Multiple Classes. In the Proceedings of the International Joint Conference on Neural Networks (IJCNN), IEEE world congress on computational Intelligence, Glasgow, UK, 2020. [pdf]
[14] Chenguang Xiao and Shuo Wang. An Experimental Study of Class Imbalance in Federated Learning. In IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, USA, 2021. [pdf]
[15] Xudong Zhang, Haoyu Wang, Shuo Wang, Yanliang Liu, Weidong Yu and Xiaofeng Li. A Machine-learning-based Model to Inverse Internal Solitary Wave Amplitude from Satellite Image. Photonics & Electromagnetics Research Symposium (PIERS), Hangzhou, China, 2022. [pdf]
[16] Zhaoyang Wang and Shuo Wang. Online Automated Machine Learning for Class Imbalanced Data Streams. In the Proceedings of the International Joint Conference on Neural Networks (IJCNN), Australia, 2023. [link]
[17] Chenguang Xiao and Shuo Wang. Triplets Oversampling for Class Imbalanced Federated Datasets. In the Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Italy, 2023 (accepted on 6th of June). [link]
[18] Guanhui Yang, Xiaoting Chen, Tengsen Zhang, Shuo Wang and Yun Yang. An Impact Study of Concept Drift in Federated Learning, In the Proceedings of the 23rd IEEE International Conference on Data Mining (ICDM), Shanghai, China, 2023.
[19] Huanbo Lyu, Daniel Herring, Lingfeng Wang, Jelena Ninic, James Andrews, Miqing Li, Michal Kocvara, Fabian Spill and Shuo Wang. Multi-Objective Optimization for Flexible Building Space Usage. In the Proceedings of IEEE Conference on Artificial Intelligence (CAI), Singapore, 2024.
[20] Sean Groom, Dan Morris, Liam Anderson and Shuo Wang. Modeling Defensive Dynamics in Football: A Hidden Markov Model-Based Approach for Man-Marking and Zonal Defending Corner Analysis. The 2nd International Workshop on Intelligent Technologies for Precision Sports Science (IT4PSS) in Conjunction with the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), South Korea, 2024. (Accepted 5th of June, 2024, in collaboration with Nottingham Forest Football Club).
[21] Zhaoyang Wang and Shuo Wang. Online Automated Imbalanced Learning via Adaptive Thompson Sampling. The 27th International Conference on Pattern Recognition (ICPR'24), India. (Accepted 17th of August, 2024)
[22] Zhaoyang Wang and Shuo Wang. Zero-shot Automated Class Imbalanced Learning. The 27th International Conference on Pattern Recognition (ICPR'24), India. (Accepted 17th of August, 2024)
[23] Guanhui Yang, Xiaoting Chen, Tengsen Zhang and Shuo Wang. A Multi-Model Approach for Handling Concept Drifting Data in Federated Learning. Workshop on Distributed Machine Learning and Unlearning for Sensor-Cloud Systems (DLS2) in conjunction with the 20th International Conference on Mobility, Sensing and Networking (MSN2024), China, 2024.
[24] Xiaoting Chen, Shuo Wang, Tengsen Zhang and Guanhui Yang. A Study of Virtual Concept Drift in Federated Data Stream Learning. The 2nd Workshop on Distributed and Integrated Communication, Sensing, and Computing in Space-Air-Ground Integrated Networks for 6G (DICSC) in conjunction with the 20th International Conference on Mobility, Sensing and Networking (MSN2024), China, 2024.
[25] Tengsen Zhang, Xiaoting Chen, Guanhui Yang and Shuo Wang. Overcoming Dynamic Class Imbalance in Federated Data Stream Learning. Workshop on Network Meets Intelligent Computations (NMIC) in conjunction with the 20th International Conference on Mobility, Sensing and Networking (MSN 2024), China, 2024.
[26] Liyan Yang, Shiqiao Zhou, Jingwei Cheng, Fu Zhang, Jicheng Wan, Shuo Wang and Mark Lee. DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation. In the 31st International Conference on Computational Linguistics, Abu Dhabi, UAE, 2024.
[27] Chenguang Xiao, Zheming Zuo, Shuo Wang. FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation. The 1st Workshop on Federated Learning for Unbounded and Intelligent Decentralization (FLUID) in conjunction with AAAI 2025.
[28] Zhaoyang Wang, Shuo Wang, Damien Ernst. Automated Class Imbalance Learning via Few-shot Bayesian Optimization with Meta-learned Deep Kernel Surrogates. The International Joint Conference on Neural Networks (IJCNN'25), Rome, Italy, 2025.
[29] Hao Li and Shuo Wang. A Two-Stage Multi-Source Transfer Learning Approach for Software Defect Prediction. The 23rd IEEE International Conference on Industrial Informatics (INDIN), Kunming, China, 2025.
[30] Xudong Guo and Shuo Wang. FedPAC: A Federated Semi-Supervised Learning Approach for Non-IID Data with Feature Shift. The IEEE International Conference on Systems, Man, and Cybernetics (SMC), Vienna, Austria, 2025.
[31] Shiqiao Zhou, Holger Schöner, Huanbo Lyu, Edouard Fouché and Shuo Wang. BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting. The 34th ACM International Conference on Information and Knowledge Management, Seoul, Korea, 2025.
[32] Hubo Yuan and Shuo Wang. SSDIR: A Novel Semi-Supervised Approach for Data Imbalanced Regression. The 14th International Conference on Computing and Pattern Recognition (ICCPR 2025), Beijing, China, 2025.
[33] Shiqiao Zhou, Holger Schöner, Zipeng Wu, Edouard Fouché, Iag Wilson and Shuo Wang. Stationarity-Aware Retrieval-Augmented Time Series Forecasting. The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'26), Jeju, Korea, 2026.
[34] Shiqiao Zhou, Zipeng Wu, Holger Schöner, Edouard Fouché, Iag Wilson and Shuo Wang. Semantics-enhanced retrieval-augmented time series forecasting. The ICML 2026 Workshop: Forecasting as a New Frontier of Intelligence, Seoul, Korea, 2026.
[35] Sean Groom, Michael Groom, Fancisco Belo, Axl Rice, Liam Anderson, Victor-Alexandru Darvariu and Shuo Wang. Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning. The 13th Workshop on the Machine Learning and Data Mining for Sports Analytics (MLSA), in conjunction with ECML/PKDD'26, Naples, Italy (Accepted on 16th of July), 2026.
[36] Bakhyt Bakiyev, Shuo Wang and Mubashir Ali. Model Comparison for Kazakh Semantic Reuse Detection Across Pairwise, News, and Long-Document Evaluation. The Conference and Labs of the Evaluation Forum (CLEF), Jena, Germany, 2026.
Book Chapters
S. Wang, G. Nebehay, L. Esterle, K. Nymoen, and L. L. Minku. Common Techniques for Self-Awareness and Self-Expression (Chapter 7) in "Self-aware Computing Systems: An Engineering Approach", Springer. Pages 113-142, 2016.
Tutorials
1. L. Minku, S. Wang and G. Boracchi. "Learning Class Imbalanced Data Streams", World Congress on Computational Intelligence (WCCI), July 2018. Slides here.
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