时间：2024年4月1日（周一） 13:00-14:30

Time: 13:00-14:30, Monday, April 1 2024

地点：西湖大学云谷校区E10-205

Venue: E10-205, Yungu Campus

主持人: 西湖大学工学院讲席教授 金耀初

Host: Chair Professor Yaochu Jin, School of Engineering

语言：英文

Language: English

主讲嘉宾/Speaker：

Prof. Shengxiang Yang 杨圣祥

Professor

School of Computer Science and Informatics

De Montfort University

主讲人简介/Biography:

Shengxiang Yang got his PhD degree in Control Theory and Control Engineering from Northeastern University, China in 1999. He is now a Professor of Computational Intelligence and Deputy Director of the Institute of Artificial Intelligence (IAI), School of Computer Science and Informatics, De Montfort University, UK. He has worked extensively for many years in the areas of CI methods, including evolutionary computation (EC), artificial neural networks, data mining and data stream analysis, and their applications for real-world problems. He has over 430 publications with an H-index of 73 and over 19,500 citations according to Google Scholar. He was named in the Stanford University World's Top 2% of Scientists List from 2020 to 2023. His work has been supported by UK research councils, EU FP7 and Horizon 2020, and industry partners. He serves as an Associate Editor or Editorial Board Member of several prestigious international journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Information Sciences, and Enterprise Information Systems, etc. He was the founding chair of the Task Force on Intelligent Network Systems (TF-INS, 2012-2017) and the chair of the Task Force on EC in Dynamic and Uncertain Environments (ECiDUEs, 2011-2017) of the IEEE Computational Intelligence Society.  He has given over 30 invited keynote speeches and tutorials at international conferences..

讲座摘要/Abstract:

Data stream mining is a natural and necessary progression from traditional data mining. However, it presents additional challenges to batch analysis: along with strict time and memory constraints, change is a major consideration. In a dynamic data stream, the underlying concepts may drift and change over time. The challenge of recognizing and reacting to change in a stream is compounded by the scarcity of labels problem. This talk presents our recent work to evaluate unsupervised learning as the basis for online classification in dynamic data streams with a scarcity of labels. A novel stream clustering algorithm based on the collective behavior of ants, called Ant Colony Stream Clustering (ACSC), is present. Furthermore, a novel framework, Clustering and One class Classification Ensemble Learning (COCEL), for classification in dynamic streams with a scarcity of labels is described. The proposed framework can identify and react to change in a stream and hugely reduces the number of required labels (typically less than 0.05% of the entire stream). Finally, some conclusions will be made.

讲座联系人/Contact:

冯如意

fengruyi@westlake.edu.cn