Shuo Wang

Research output

Research output per year 2009 2013 2018 2019 2022 2023 2024

• 23 Article
• 14 Conference contribution
• 7 Paper
• 2 Editorial
• 1 More
  • 1 Chapter

Research output per year

Research output per year

Search results

• 2024

  An Impact Study of Concept Drift in Federated Learning

  Yang, G., Chen, X., Zhang, T., Wang, S. & Yang, Y., 5 Feb 2024, 2023 IEEE International Conference on Data Mining (ICDM). IEEE, p. 1457-1462 6 p. (IEEE International Conference on Data Mining (ICDM)).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Data streams 100%
  • Machine learning 72%
  33 Downloads (Pure)
• 2023

  Online automated machine learning for class imbalanced data streams

  Wang, Z. & Wang, S., 2 Aug 2023, 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, p. 1-8 8 p. (International Joint Conference on Neural Networks (IJCNN)).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Data streams 100%
  • Machine learning 72%
  • Intrusion detection 11%
  • Failure analysis 9%
  55 Downloads (Pure)

• Triplets Oversampling for Class Imbalanced Federated Datasets

  Xiao, C. & Wang, S., 17 Sept 2023, Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II. Koutra, D., Plant, C., Rodriguez, M. G., Baralis, E. & Bonchi, F. (eds.). 1 ed. Springer, p. 368–383 16 p. (Lecture Notes in Computer Science; vol. 14170).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Data privacy 100%
  • Learning systems 99%
  • Machine learning 71%
  • Sampling 68%
  • Experiments 34%
  1 Downloads (Pure)
• 2022

  4th Workshop on Learning with Imbalanced Domains - Theory and Applications: Preface

  Moniz, N., Branco, P., Torgo, L., Japkowicz, N., Wozniak, M. & Wang, S., 23 Sept 2022, Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, 23 September 2022, ECML-PKDD, Grenoble, France. Moniz, N., Branco, P., Torgo, L., Japkowicz, N., Wozniak, M. & Wang, S. (eds.). Proceedings of Machine Learning Research, p. 1-7 7 p. (Proceedings of Machine Learning Research; vol. 183).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File
  25 Downloads (Pure)

• A Machine-learning-based Model to Inverse Internal Solitary Wave Amplitude from Satellite Image

  Zhang, X., Wang, H., Wang, S., Liu, Y., Yu, W. & Li, X., 16 Jun 2022, 2022 Photonics & Electromagnetics Research Symposium (PIERS). Institute of Electrical and Electronics Engineers (IEEE), p. 269-272 4 p. (Progress in Electromagnetics Research Symposium).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Solitons 100%
  • Wave 66%
  • Satellites 54%
  • Machine learning 52%
  • Energy 5%

• An experimental study of class imbalance in federated learning

  Xiao, C. & Wang, S., 24 Jan 2022, 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Institute of Electrical and Electronics Engineers (IEEE), 7 p. 9660072. (IEEE Symposium Series on Computational Intelligence).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Experimental Study 100%
  • Imbalance 75%
  • Learning 72%
  • Global Model 38%
  • Class 23%
  60 Downloads (Pure)
• 2021

  3rd Workshop on Learning with Imbalanced Domains: Preface

  Moniz, N., Branco, P., Torgo, L., Japkowicz, N., Woźniak, M. & Wang, S., 17 Sept 2021, Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, 17 September 2021, ECML-PKDD, Bilbao (Basque Country, Spain). Moniz, N., Branco, P., Torgo, L., Japkowicz, N., Woźniak, M. & Wang, S. (eds.). Proceedings of Machine Learning Research, p. 1-6 6 p. (Proceedings of Machine Learning Research; vol. 154).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File
  23 Downloads (Pure)
• 2020

  AUC estimation and concept drift detection for imbalanced data streams with multiple classes

  Wang, S. & Minku, L., 28 Sept 2020, Proceedings of the International Joint Conference on Neural Networks (IJCNN), World Congress on Computational Intelligence, 2020. IEEE Computer Society Press, 8 p. 9207377. (Proceedings of International Joint Conference on Neural Networks).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

  • Data streams 100%
  • Classifiers 17%
  • Experiments 8%
  291 Downloads (Pure)
• 2019

  Concept drift detection in histogram-based straightforward data stream prediction

  Casillas, J., Wang, S. & Yao, X., 7 Feb 2019, Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018. Yu, J., Li, Z., Zhu, F. & Tong, H. (eds.). IEEE Computer Society Press, p. 878-885 8 p. 8637546. (IEEE International Conference on Data Mining Workshops, ICDMW; vol. 2018-November).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Data streams 100%
  • Labels 41%
  • Detectors 38%
  • Monitoring 27%
  2 Citations (Scopus)

• Understanding the Automated Parameter Optimization on Transfer Learning for Cross-Project Defect Prediction: An Empirical Study

  Li, K., Xiang, Z., Chen, T., Wang, S. & Tan, K. C., 9 Dec 2019, (Accepted/In press) 42nd International Conference on Software Engineering (ICSE 2020). Association for Computing Machinery (ACM), 12 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Transfer learning 100%
  • Defects 57%
  • Learning algorithms 4%
  • Software engineering 4%
  • Costs 4%
  3 Citations (Scopus)
• 2018

  To adapt or not to adapt? Technical debt and learning driven self-adaptation for managing runtime performance

  Chen, T., Bahsoon, R., Wang, S. & Yao, X., 30 Mar 2018, ICPE 2018 - Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering. Association for Computing Machinery , p. 48-55 8 p. (ICPE 2018 - Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering; vol. 2018-March).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Adaptive systems 100%
  • Machine learning 33%
  • Classifiers 32%
  • Labeling 22%
  • Labels 19%
  23 Citations (Scopus)
• 2016

  Dealing with Multiple Classes in Online Class Imbalance Learning

  WANG, S., MINKU, L. L. & YAO, X., 15 Jul 2016, Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). New York City: AAAI Press, p. 2118-2124 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  • Data streams 100%
  • Sampling 69%
  30 Citations (Scopus)
• 2014

  A multi-objective ensemble method for online class imbalance learning

  Wang, S., Minku, L. L. & Yao, X., 3 Sept 2014, Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers (IEEE), p. 3311-3318 8 p. 6889545

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Data streams 100%
  • Experiments 7%
  11 Citations (Scopus)
• 2010

  The effectiveness of a new negative correlation learning algorithm for classification ensembles

  Wang, S. & Yao, X., 2010, Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010. p. 1013-1020 8 p. 5693406. (Proceedings - IEEE International Conference on Data Mining, ICDM).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  • Adaptive boosting 100%
  • Learning algorithms 70%
  • Set theory 7%
  • Costs 4%
  • Experiments 4%
  8 Citations (Scopus)