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

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.

Citation

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: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 302-307
• Series ISSN: 2573-3311
• DOI: https://doi.org/10.1109/ICACI.2019.8778594
• Keywords: Variational inference; Random projection; Ensemble method; Online learning; Multi-label data stream
• Public URL: https://rgu-repository.worktribe.com/output/363846