@inproceedings { , title = {An online variational inference and ensemble based multi-label classifier for data streams.}, abstract = {Recently, multi-label classification algorithms have been increasingly required by a diversity of applications, such as text categorization, web, and social media mining. In particular, these applications often have streams of data coming continuously, and require learning and predicting done on-the-fly. In this paper, we introduce a scalable online variational inference based ensemble method for classifying multi-label data, where random projections are used to create the ensemble system. As a second-order generative method, the proposed classifier can effectively exploit the underlying structure of the data during learning. Experiments on several real-world datasets demonstrate the superior performance of our new method over several well-known methods in the literature.}, conference = {11th International conference on advanced computational intelligence (ICACI 2019)}, doi = {10.1109/ICACI.2019.8778594}, note = {INFO COMPLETE (checked, info via Scopus 22/8/2019 LM) PERMISSION GRANTED (version = AAM; embargo = none; licence = BY-NC) DOCUMENT RECEIVED (AAM rec'd from contact 23/8/2019 LM) ADDITIONAL INFORMATION (An accepted article is a version which has been revised by the author to incorporate review suggestions, and which has been accepted by IEEE for publication. If peer review by a journal or a conference requires no changes for publication, the accepted version is identical to the version initially submitted by the author)}, pages = {302-307}, publicationstatus = {Published}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, url = {https://rgu-repository.worktribe.com/output/363846}, keyword = {Variational inference, Random projection, Ensemble method, Online learning, Multi-label data stream}, year = {2019}, author = {Nguyen, Thi Thu Thuy and Nguyen, Tien Thanh and Liew, Alan Wee-Chung and Wang, Shi-Lin and Liang, Tiancai and Hu, Yongjiang} }