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Xiaoming Zhang: Class Imbalance Learning Problem, Modelling and Challenges

报告人: Professor Xiaoming Zhang
讲者简介: 香港浸会大学张晓明教授是 IEEE Fellow,IET/IEE Fellow,英国计算机学会 Fellow, 皇家文艺制造商业学会 Fellow 以及香港国际工程技术协会杰出 Fellow,IEEE 香港计算智能(前身为神经网络) 学会始创者及前任主席, IEEE 计算机学会智能信息学委员会 (TCII)现任主席,而且也是香港浸会大学计算和理论科学研究所的副所长。张晓明教 授长期从事人工智能、模式识别、图像及视频处理,函数优化等研究,在相关国际著名 期刊及学术会议上,如 IEEE Transactions on Pattern Analysis and Machine Intelligence,IEEE Transactions on Information Forensics and Security, IEEE Transactions on Image Processing,IEEE Transactions on Knowledge and Data Engineering,IEEE Transactions on Neural Networks, IEEE Transactions on Circuits and Systems for Video Technology,CVPR, IJCAI, AAAI 等已发表论文逾 220 篇,曾获 IWDVT’2005、ICNC-FSKD
报告地点: Room 2403,East Main Building of Guang-Hua Tower
报告时间: 2018.8.17,
报告摘要: In many practical problems, number of data form difference classes can be quite imbalanced, which could make the performance of the most machine learning methods become deteriorate to a certain degree. As far as we know, the problem of learning from imbalanced data continues to be one of the challenges in the field of data engineering and machine learning, which has attracted growing attentions in recent years. In this talk, we will first formally describe the class imbalance problem and its significance with examples from real world applications, and review the existing solutions. Then, two research problems, i.e. classifier weights of boosting and imbalanced streaming data with concept drift, are studied. Accordingly, we have proposed a solution for each problem. Finally, some challenging problems in this topic are explored as well.