Outputs (93)

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..

Racing strategy for the dynamic-customer location-allocation problem. (2020)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A., CONWAY, A. and OWUSU, G. 2020. Racing strategy for the dynamic-customer location-allocation problem. 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 9185918. Available from: https://doi.org/10.1109/CEC48606.2020.9185918

In previous work, we proposed and studied a new dynamic formulation of the Location-allocation (LA) problem called the Dynamic-Customer Location-allocation (DC-LA) prob­lem. DC-LA is based on the idea of changes in customer distribution over a define... Read More about Racing strategy for the dynamic-customer location-allocation problem..

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..

Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. (2020)
Journal Article
BROWN, C. JANJIC, V., GOLI, M. and MCCALL, J. 2020. Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. International journal of parallel programming [online], 48(4): high level parallel programming, pages 583-602. Available from: https://doi.org/10.1007/s10766-020-00665-z

This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree s... Read More about Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search..

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..

Confidence in prediction: an approach for dynamic weighted ensemble. (2020)
Conference Proceeding
DO D.T., NGUYEN T.T., NGUYEN T.T., LUONG A.V., LIEW A.W.-C. and MCCALL J. 2020. Confidence in prediction: an approach for dynamic weighted ensemble. In Nguyen N., Jearanaitanakij K., Selamat A., Trawiński B. and Chittayasothorn S. (eds.) Intelligent information and database systems: proceedings of the 12th Asian intelligent information and database systems conference (ACIIDS 2020), 23-26 March 2020, Phuket, Thailand. Lecture Notes in Computer Science, 12033. Cham: Springer [online], part 1, pages 358-370. Available from: https://doi.org/10.1007/978-3-030-41964-6_31

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this... Read More about Confidence in prediction: an approach for dynamic weighted ensemble..

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..

Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry. (2019)
Thesis
ANKRAH, R.B. 2019. Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

The location-allocation (LA) problem concerns the location of facilities and the allocation of demand, to minimise or maximise a particular function such as cost, profit or a measure of distance. Many formulations of LA problems have been presented i... Read More about Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry..

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..