报告题目：Data Stream Learning: Challenges, Solutions and Future
主 讲 人：王硕，英国伯明翰大学，助理教授
报告时间：2023年4月25日（星期二）16:00-17:30
报告地点：腾讯会议825-691-093
报告摘要：
In many real-world applications, data are generated or collected continuously in the form of data streams, such as social media data analysis, spam detection and IoT systems. Data stream learning is such a machine learning area that processes data streams, trains a model and makes predictions over time. In particular, class imbalance and concept drift are two major challenges in data stream learning. In this talk, data stream learning and the two challenges will be introduced. How class imbalance and concept drift in data streams are tackled will be elaborated, including some applications. The talk will end with a short discussion of future research directions in this area.
报告人简介：
Shuo Wang is an a
ssistant professor at School of Computer Science, the University of Birmingham, UK. She was a research fellow at the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) in the University of Birmingham between 2011 and 2018. Her research interests include data stream classification, class imbalance learning and ensemble learning approaches in machine learning, and their applications in social media analysis, software engineering and fault detection. Her work has been published in internationally renowned journals and conferences, such as IEEE Transactions on Knowledge and Data Engineering and International Joint Conference on Artificial Intelligence (IJCAI). In addition, she was a guest editor of Neurocomputing and Connection Science and the workshop organizer of IJCAI'17 and ECML/PKDD’21,’22. She is currently in the editorial board of International Journal of Computational Intelligence and Applications.
邀请人：计算机科学与技术系 卢杨助理教授