All Outputs (3)

A novel ensemble aggregation method based on deep learning representation. (2024)
Presentation / Conference Contribution
NGUYEN, T.T., ELYAN, E., DANG, T., NGUYEN, T.T. and LONGMUIR, M. 2025. A novel ensemble aggregation method based on deep learning representation. In Antonacopoulos, A., Chaudhuri, S., Chellappa, R., et al. (eds.) Pattern recognition: proceedings of the 27th International conference on pattern recognition, 01-05 December 2024, Kolkata, India. Lecture notes in computer science, 15324. Cham: Springer [online], pages 31-46. Available from: https://doi.org/10.1007/978-3-031-78383-8_3

We propose a novel ensemble aggregation method by using a deep learning-based representation approach. Specifically, we applied the Cross-Validation procedure on training data with a number of learning algorithms to obtain the predictions for trainin... Read More about A novel ensemble aggregation method based on deep learning representation..

A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture. (2024)
Presentation / Conference Contribution
DANG, T., NGUYEN, T.T., MCCALL, J., HAN, K. and LIEW, A.W.-C. 2024. A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2024), 30 June - 05 July 2024, Yokohama, Japan. Available from: https://doi.org/10.1109/CEC60901.2024.10611960

Deep neural networks (DNN) has achieved great successes across multiple domains. In recent years, a number of approaches have emerged on automatically finding the optimal DNN configurations. A technique among these approaches which show great promise... Read More about A novel surrogate model for variable-length encoding and its application in optimising deep learning architecture..

VISTA: a variable length genetic algorithm and LSTM-based surrogate assisted ensemble selection algorithm in multiple layers ensemble system. (2024)
Presentation / Conference Contribution
HAN, K., NGUYEN, T.T., VU, V.A., LIEW, A.W.-C., DANG, T. and NGUYEN, T.T. 2024. VISTA: a variable length genetic algorithm and LSTM-based surrogate assisted ensemble selection algorithm in multiple layers ensemble system. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Congress on evolutionary computation (CEC 2024), 30 June - 05 July 2024, Yokohama, Japan. Piscataway: IEEE [online], article 10612029. Available from: https://doi.org/10.1109/CEC60901.2024.10612029

We proposed a novel ensemble selection method called VISTA for multiple layers ensemble systems (MLES). Our ensemble model consists of multiple layers of ensemble of classifiers (EoC) in which the EoC in each layer is trained on the data generated by... Read More about VISTA: a variable length genetic algorithm and LSTM-based surrogate assisted ensemble selection algorithm in multiple layers ensemble system..