Prof. Zhiwen Yu (IEEE Senior Member)

South China University of Technology, China

Zhiwen Yu  is a Professor in School of Computer Science and Engineering, South China University of Technology, China. He received the Ph.D. degree from the City University of Hong Kong, Hong Kong, in 2008. Dr. Yu has authored or coauthored more than 200 refereed journal articles and international conference papers, including more than 80 articles in the journals of IEEE Transactions, h-index 56，Google citation 16000. He is an Associate Editor of the IEEE Transactions on systems, man, and cybernetics: systems, and the informatics journal. Dr. Yu is in charge of or take part in more than 30 research projects, such as the National Natural Science Foundation of China (the Key Program、the General Program and the Youth Program), National Natural Science Foundation of China for Excellent Young Scientists, the Key R&D Program of Guang Dong Province, and so on. He is a senior member of IEEE and ACM, a Member of the Council of China Computer Federation from 2016 to 2023 (CCF). Ranked among the top 2% of scientists worldwide in Stanford University's World's Top 2% Scientists List from 2022 to 2025.

Title: Imbalanced learning theory and applications

Abstract: Imbalanced learning is a critical task in machine learning, addressing datasets with significant class disparities, which are prevalent in applications such as healthcare, finance, and security. While balanced datasets are ideal for training robust classifiers, the reality of skewed class distributions in real-world scenarios makes imbalanced learning essential for effective model performance. One of the primary challenges in this domain is the classifier’s tendency to favor the majority class, resulting in poor predictive accuracy for the minority class, which is often of greater interest. In many cases, only partial or indirect indicators of the underlying class imbalance—such as feature distributions or data sampling biases—are available, complicating the learning process. This talk will discuss recent research on innovative techniques to tackle these challenges. For instance, the Classifier Ensemble Based on Multiview Optimization addresses high-dimensional imbalanced data by optimizing multiple sub-views to reduce feature redundancy and enhance classification performance. Similarly, the Imbalance Large Margin Nearest Neighbor algorithm improves feature spaces through metric learning and oversampling, enabling better separation of minority and majority classes. These methods effectively model the latent structure of class disparities and mitigate the bias toward the majority class, leading to more accurate and robust classifiers.