模式识别国家重点实验室
中国科学院自动化研究所   设为首页  加入收藏  联系我们
 
English
网站首页      实验室概况      研究队伍      组织机构      学术交流      科研成果      人才培养      开放课题      创新文化      资源共享      联系我们
    学术讲座

Ensemble Approaches to Class Imbalance Learning

模式识别学术大讲堂
Advanced Lecture Series in Pattern Recognition
题    目 (TITLE):Ensemble Approaches to Class Imbalance Learning
讲 座 人 (SPEAKER):Prof. Xin Yao (University of Birmingham, UK)
主 持 人 (CHAIR): Prof. Chenglin Liu
时    间 (TIME):May 19(Thursday), 2016, 10:30 AM
地    点 (VENUE):No.2 Conference Room (3rd floor), Intelligence Building
报告摘要(ABSTRACT):
Many real-world classification problems have unbalanced classes, e.g., in fault detection and software defect prediction, where there are a large number of training examples for the normal class, but few for the abnormal classes. This talk gives an overview of some recent algorithms for dealing with class imbalance in machine learning, including ensemble approaches, sampling methods, evolutionary computation methods, and their combinations. First, we will discuss how diversity influences the classification performance, especially on the minority class, in ensemble classification algorithms. Then new ensemble algorithms are introduced and evaluated experimentally. Multi-class imbalance will be analysed and considered. The combination of ensemble learning and sampling techniques for dealing with class imbalance will be presented. Finally, we consider a new problem --- online class imbalance learning of data streams, where the majority and minority classes are not pre-defined and have to be learned and detected online. Some results are presented to demonstrate the effectiveness of the proposed algorithm.
报告人简介(BIOGRAPHY):
Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the Past President (2014-15) of IEEE Computational Intelligence Society (CIS).
His work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award,
and many other best paper awards. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the 2013 IEEE CIS Evolutionary Computation Pioneer Award. He was the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation and is an Associate Editor or Editorial Member of more than ten other journals. His major research interests include
evolutionary computation, ensemble learning, and their applications, especially in software engineering.

友情链接
 
中科院自动化研究所 模式识别国家重点实验室
NLPR, INSTITUTE OF AUTOMATION, CHNESE ACADEMY OF SCIENCES