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Dr Thanh Nguyen


Streaming multi-layer ensemble selection using dynamic genetic algorithm. (2021)
Conference Proceeding
LUONG, A.V., NGUYEN, T.T. and LIEW, A.W.-C. 2021. Streaming multi-layer ensemble selection using dynamic genetic algorithm. In Zhou, J., Salvado, O., Sohel, F., Borges, P. and Wang, S. (eds.). Proceedings of 2021 Digital image computing: techniques and applications (DICTA 2021), 29 November - 1 December 2021, Gold Coast, Australia. Piscataway: IEEE [online], article 9647220. Available from: https://doi.org/10.1109/dicta52665.2021.9647220

In this study, we introduce a novel framework for non-stationary data stream classification problems by modifying the Genetic Algorithm to search for the optimal configuration of a streaming multi-layer ensemble. We aim to connect the two sub-fields... Read More about Streaming multi-layer ensemble selection using dynamic genetic algorithm..

Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. (2021)
Conference Proceeding
DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, Kraków, Poland : [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorith... Read More about Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation..

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..

A homogeneous-heterogeneous ensemble of classifiers. (2020)
Conference Proceeding
LUONG, A.V., VU, T.H., NGUYEN, P.M., VAN PHAM, N., MCCALL, J., LIEW, A.W.-C. and NGUYEN, T.T. 2020. A homogeneous-heterogeneous ensemble of classifiers. 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, 251-259. Available from: https://doi.org/10.1007/978-3-030-63823-8_30

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each ot... Read More about A homogeneous-heterogeneous ensemble of classifiers..

Heterogeneous ensemble selection for evolving data streams. (2020)
Journal Article
LUONG, A.V., NGUYEN, T.T., LIEW, A.W.-C. and WANG, S. 2021. Heterogeneous ensemble selection for evolving data streams. Pattern recognition [online], 112, article ID 107743. Available from: https://doi.org/10.1016/j.patcog.2020.107743

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model.... Read More about Heterogeneous ensemble selection for evolving data streams..

Heterogeneous ensemble selection for evolving data streams. [Dataset] (2020)
Dataset
LUONG, A.V., NGUYEN, T.T., LIEW, A.W.-C. and WANG, S. 2021. Heterogeneous ensemble selection for evolving data streams. [Dataset]. Pattern recognition [online], 112, article ID 107743. Available from: https://www.sciencedirect.com/science/article/pii/S003132032030546X#sec0023

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model.... Read More about Heterogeneous ensemble selection for evolving data streams. [Dataset].

WEC: weighted ensemble of text classifiers. (2020)
Conference Proceeding
UPADHYAY, A., NGUYEN, T.T., MASSIE, S. and MCCALL, J. 2020. WEC: weighted ensemble of text classifiers. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article ID 9185641. Available from: https://doi.org/10.1109/CEC48606.2020.9185641

Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the c... Read More about WEC: weighted ensemble of text classifiers..

Evolved ensemble of detectors for gross error detection. (2020)
Conference Proceeding
NGUYEN, T.T., MCCALL, J., WILSON, A., OCHEI, L., CORBETT, H. and STOCKTON, P. 2020. Evolved ensemble of detectors for gross error detection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference companion (GECCO 2020), 8-12 July 2020, Cancún, Mexico. New York: ACM [online], pages 281-282. Available from: https://doi.org/10.1145/3377929.3389906

In this study, we evolve an ensemble of detectors to check the presence of gross systematic errors on measurement data. We use the Fisher method to combine the output of different detectors and then test the hypothesis about the presence of gross err... Read More about Evolved ensemble of detectors for gross error detection..

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..