Shuo Wang

Lecturer

School of Computing and Digital Technology

Email:: shuo.wang@bcu.ac.uk

Shuo Wang is a lecturer at School of Computing and Digital Technology, Birmingham City University (UK), and also an Honorary Research Fellow at the School of Computer Science, the University of Birmingham (UK). She received the Ph.D. degree in Computer Science from the University of Birmingham, U.K., in 2011, sponsored by the Overseas Research Students Award (ORSAS) from the British Government (2007). She has been involved in two EPSRC-funded projects and three EU-funded projects. Her research interests include class imbalance learning, ensemble learning, online learning and machine learning in software engineering and social media analysis. Her work has been published in internationally renowned journals and conferences, such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems, and International Joint Conference on Artificial Intelligence (IJCAI). Her leading research on multi-class imbalance problems and tackling class imbalance in software defect prediction published in 2012 and 2013 have received over 200 citations.

Among other roles, she successfully organized and chaired the first workshop on learning in the presence of class imbalance and concept drift, IJCAI’17; she is the guest editor for the Neurocomputing on Learning in the Presence of Class Imbalance and Concept Drift, and the guest editor for the Connection Science on Learning from Data Streams and Class Imbalance. A tutorial on Learning Class Imbalanced Data Streams was given at IEEE World Congress on Computational Intelligence (WCCI), Rio de Janeiro, Brazil, 2018.

Areas of Expertise

Qualifications

Memberships

Teaching

Research

Publications

Journal papers

[1] 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, 2018.

[2] 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, DOI: 10.1109/TNNLS.2017.2771290, 2018. [pdf]

[3] 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, DOI: 10.1109/TFUZZ.2018.2814591, 2018. [pdf]

[4] 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, DOI: 10.1109/TCYB.2017.2739338, August 2017. [pdf]

[5] 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]

[6] 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]

[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] S. Wang, L.L.Minku and X. 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).

[9] 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.

[10] S. Wang, L.L.Minku and X. 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]

[11] S. Wang and X. Yao, "Using Class Imbalance Learning for Software Defect Prediction", IEEE Transactions on Reliability, 62(2):434-443, 2012. [pdf].

Code is available [here] in Java (weka 3.7 required).

[12] S. Wang and X. Yao, "Multi-Class Imbalance Problems: Analysis and Potential Solutions", IEEE Transactions on Systems, Man and Cybernetics, PartB: Cybernetics, 42(4):1119-1130, August 2012. [pdf] or [online].

Code is available [here] in Java (weka 3.7 required).

[13] S. Wang and X. 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. [pdf] or [online].

Conference papers

[1] 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]

[2] 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]

[3] 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).

[4] 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]

[5] 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]

[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 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]

[8] 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]

[9] 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]

[10] 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]

[11] 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]

Book chapter

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.

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