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Evolving an optimal decision template for combining classifiers. (2019)
Conference Proceeding
NGUYEN, T.T., LUONG, A.V., DANG, M.T., DAO, L.P., NGUYEN, T.T.T., LIEW, A.W.-C. and MCCALL, J. 2019. Evolving an optimal decision template for combining classifiers. In Gedeon, T., Wong, K.W. and Lee, M. (eds.) Neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia. Part I. Lecture notes in computer science, 11953. Cham: Springer [online], pages 608-620. Available from: https://doi.org/10.1007/978-3-030-36708-4_50

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets l... Read More about Evolving an optimal decision template for combining classifiers..

An online variational inference and ensemble based multi-label classifier for data streams. (2019)
Conference Proceeding
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

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 continuousl... Read More about An online variational inference and ensemble based multi-label classifier for data streams..

Simultaneous meta-data and meta-classifier selection in multiple classifier system. (2019)
Conference Proceeding
NGUYEN, T.T., LUONG, A.V., NGUYEN, T.M.V., HA, T.S., LIEW, A.W.-C. and MCCALL, J. 2019. Simultaneous meta-data and meta-classifier selection in multiple classifier system. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference (GECCO ’19), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 39-46. Available from: https://doi.org/10.1145/3321707.3321770

In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significan... Read More about Simultaneous meta-data and meta-classifier selection in multiple classifier system..