In many real data mining tasks, one data object is often associated with multiple class labels simultaneously; for example, a document may belong to multiple topics, an image can be tagged with multiple terms, etc. Multi-label learning focuses on such problems, and it is well accepted that the exploitation of relationship among labels is crucial; actually this is the essential di erence between multi-label learning and conventional (single-label) supervised learning. Most multi-label learning approaches try to capture label relationship and then apply it to help construct prediction models. Some approaches rely on external knowledge resources such as label hierarchies, and some approaches try to exploit label relationship by counting the label co-occurrences in training data. These approaches are e ective in many cases; however, in real practice, the external label relationship is often unavailable, and generating label relationship from training data and then applying to the same training data for model construction will greatly increase the over tting risk. Moreover, the label relationship is usually assumed symmetric, and almost all existing approaches exploit it globally by assuming the label correlation be shared among all instances.
the National Science & Technology Award for Young Scholars of China, and many other awards including nine international journal/conference paper or competition awards. He is an Associate Editor-in-Chief of "Chinese Science Bulletin", Associate Editor or Editorial Boards member of "ACM Trans. Intelligent Systems and Technology" and twelve other journals. He is the Founder and Steering Committee Chair of ACML, and Steering Committee member of PAKDD and PRICAI. He is the Chair of the AI&PR Technical Committee of the China Computer Federation, Chair of the Machine Learning Technical Committee of the China Association of AI, the Vice Chair of the Data Mining Technical Committee of the IEEE Computational Intelligence Society, and the Chair of the IEEE Computer Society Nanjing Chapter. He is a Fellow of the IAPR, Fellow of the IEEE, and Fellow of the IET/IEE.