VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning. (2021)
Conference Proceeding
HAN, K., PHAM, T., VU, T.H., DANG, T., MCCALL, J. and NGUYEN, T.T. 2021. VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning. In Nguyen, N.T., Chittayasothorn, S., Niyato, D. and Trawiński, B. (eds.) Intelligent information and database systems: proceedings of the 13th Asian conference on intelligent information and database systems 2021 (ACCIIDS 2021), 7-10 April 2021, [virtual conference]. Lecture Notes in Computer Science, 12672. Cham: Springer [online], pages 168–180. Available from: https://doi.org/10.1007/978-3-030-73280-6_14

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for th... Read More about VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning..

Toward an ensemble of object detectors. (2020)
Conference Proceeding
DANG, T., NGUYEN, T.T. and MCCALL, J. 2020. Toward an ensemble of object detectors. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 458-467. Available from: https://doi.org/10.1007/978-3-030-63823-8_53

The field of object detection has witnessed great strides in recent years. With the wave of deep neural networks (DNN), many breakthroughs have achieved for the problems of object detection which previously were thought to be difficult. However, ther... Read More about Toward an ensemble of object detectors..

Multi-layer heterogeneous ensemble with classifier and feature selection. (2020)
Conference Proceeding
NGUYEN, T.T., VAN PHAM, N., DANG, M.T., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2020. Multi-layer heterogeneous ensemble with classifier and feature selection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference (GECCO 2020), 8-12 July 2020, Cancun, Mexico. New York: ACM [online], pages 725-733. Available from: https://doi.org/10.1145/3377930.3389832

Deep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep le... Read More about Multi-layer heterogeneous ensemble with classifier and feature selection..

Evolving interval-based representation for multiple classifier fusion. (2020)
Journal Article
NGUYEN, T.T., DANG,M.T., BAGHEL, V.A., LUONG, A.V., MCCALL, J. and LIEW, A.W.-C. 2020 Evolving interval-based representation for multiple classifier fusion. Knowledge-based systems [online], 201-202, article ID 106034. Available from: https://doi.org/10.1016/j.knosys.2020.106034

Designing an ensemble of classifiers is one of the popular research topics in machine learning since it can give better results than using each constituent member. Furthermore, the performance of ensemble can be improved using selection or adaptation... Read More about Evolving interval-based representation for multiple classifier fusion..

Deep heterogeneous ensemble. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., PHAM, T.D., DAO, L.P., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2019. Deep heterogeneous ensemble. Australian journal of intelligent information processing systems [online], 16(1): special issue on neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia, pages 1-9. Available from: http://ajiips.com.au/papers/V16.1/v16n1_5-13.pdf

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations... Read More about Deep heterogeneous ensemble..

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..