Thi Thu Thuy Nguyen
An online variational inference and ensemble based multi-label classifier for data streams.
Nguyen, Thi Thu Thuy; Nguyen, Tien Thanh; Liew, Alan Wee-Chung; Wang, Shi-Lin; Liang, Tiancai; Hu, Yongjiang
Dr Thanh Nguyen email@example.com
Senior Research Fellow
Alan Wee-Chung Liew
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.
NGUYEN, T.T.T., NGUYEN, T.T., LIEW, A.W.-C., WANG, S.-L., LIANG, T. and HU, Y. 2019. An online variational inference and ensemble based multi-label classifier for data streams. In Proceedings of 11th International conference on advanced computational intelligence (ICACI 2019), 7-9 June 2019, Guilin, China. Piscataway: IEEE [online], pages 302-307. Available from: https://doi.org/10.1109/ICACI.2019.8778594
|Conference Name||11th International conference on advanced computational intelligence (ICACI 2019)|
|Conference Location||Guilin, China|
|Start Date||Jun 7, 2019|
|End Date||Jun 9, 2019|
|Acceptance Date||Mar 1, 2019|
|Online Publication Date||Jul 29, 2019|
|Publication Date||Jul 29, 2019|
|Deposit Date||Aug 23, 2019|
|Publicly Available Date||Aug 23, 2019|
|Publisher||IEEE Institute of Electrical and Electronics Engineers|
|Keywords||Variational inference; Random projection; Ensemble method; Online learning; Multi-label data stream|
NGUYEN 2019 An online variational
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