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>> 09-07-2019: Big Data: An Imbalanced Learning Perspective »

>> 09-07-2019: Big Data: An Imbalanced Learning Perspective »

Big data has become an important topic worldwide over the past several years. Among many aspects of the big data research and development, imbalanced learning has become a critical component as many data sets in real-world applications are imbalanced, ranging from Internet, finance, social network, to medical and health industry. In general, the imbalanced learning problem is concerned with the performance of machine learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently and effectively into information and knowledge representation.
In this talk, I will start with an overview of the nature and foundation of the imbalanced learning problem, and then focus on the state-of-the-art methods and technologies in dealing with the imbalanced data, followed by a systematic discussion on the assessment metrics to evaluate learning performance under the imbalanced learning scenario. I will also introduce the latest research development in our group that we have developed and tested on various imbalanced data sets. Finally, I will highlight the major opportunities and challenges, as well as potential research directions for learning from imbalanced data facing the big data era.


Brief Bio:
Haibo He is a Fellow of IEEE and the Robert Haas Endowed Chair Professor at the University of Rhode Island, Kingston, RI, USA. His primary research interests include computational intelligence and various applications. He has published one sole-author book (Wiley), edited 1 book (Wiley- IEEE) and 6 conference proceedings (Springer), and authored/co-authors over 300 peer-reviewed journal and conference papers, including several highly cited papers in IEEE Transactions on Neural Networks and IEEE Transactions on Knowledge and Data Engineering, Cover Page Highlighted paper in IEEE Transactions on Information Forensics and Security, and Best Readings of the IEEE Communications Society. He has delivered more than 80 invited talks around the globe. He was the Chair of IEEE Computational Intelligence Society (CIS) Emergent Technologies Technical Committee (ETTC) (2015) and the Chair of IEEE CIS Neural Networks Technical Committee
(NNTC) (2013 and 2014). He served as the General Chair of 2014 IEEE Symposium Series on Computational Intelligence (IEEE SSCI’14, Orlando, Florida). He is currently the Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems. He was a recipient of the IEEE International Conference on Communications (ICC) “Best Paper Award” (2014), IEEE CIS “Outstanding Early Career Award” (2014), National Science Foundation “Faculty Early Career
Development (CAREER) Award” (2011), and Providence Business News (PBN) “Rising Star Innovator” Award (2011).

Lettre de la présidente


Frédérique Vallée

Un grand merci à tous les membres de la Section qui m'ont élue à la tête du nouveau Conseil d'Administration. Je suis certaine que nous allons constituer une équipe dynamique et efficace qui continuera sur la trace des équipes précédentes. Lire la suite ..

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