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Professor John McCall


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..

Ensemble selection based on classifier prediction confidence. (2019)
Journal Article
NGUYEN, T.T., LUONG, A.V., DANG, M.T., LIEW, A.W.-C. and MCCALL, J. 2020. Ensemble selection based on classifier prediction confidence. Pattern recognition [online], 100, article ID 107104. Available from: https://doi.org/10.1016/j.patcog.2019.107104

Ensemble selection is one of the most studied topics in ensemble learning because a selected subset of base classifiers may perform better than the whole ensemble system. In recent years, a great many ensemble selection methods have been introduced.... Read More about Ensemble selection based on classifier prediction confidence..

Introducing the dynamic customer location-allocation problem. (2019)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2019. Introducing the dynamic customer location-allocation problem. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) Congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, NZ. Piscataway: IEEE [online], pages 3157-3164. Available from: https://doi.org/10.1109/CEC.2019.8790150

In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customer... Read More about Introducing the dynamic customer location-allocation problem..

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..

Limitations of benchmark sets and landscape features for algorithm selection and performance prediction. (2019)
Conference Proceeding
LACROIX, B. and MCCALL, J. 2019. Limitations of benchmark sets and landscape features for algorithm selection and performance prediction. In López-Ibáñe, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference (GECCO 2019) companion, 13-17 July 2019, Prague, Czech Republic. New York: Association for Computing Machinery [online], pages 261-262. Available from: https://doi.org/10.1145/3319619.3322051

Benchmark sets and landscape features are used to test algorithms and to train models to perform algorithm selection or configuration. These approaches are based on the assumption that algorithms have similar performances on problems with similar fea... Read More about Limitations of benchmark sets and landscape features for algorithm selection and performance prediction..

On the definition of dynamic permutation problems under landscape rotation. (2019)
Conference Proceeding
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2019. On the definition of dynamic permutation problems under landscape rotation. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference companion (GECCO 2019), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 1518-1526. Available from: https://doi.org/10.1145/3319619.3326840

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In... Read More about On the definition of dynamic permutation problems under landscape rotation..

Multi-label classification via incremental clustering on an evolving data stream. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., LUONG, A.V., LIEW, A. W.-C., LIANG, T. and MCCALL, J. 2019. Multi-label classification via incremental clustering on an evolving data stream. Pattern recognition [online], 95, pages 96-113. Available from: https://doi.org/10.1016/j.patcog.2019.06.001

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make pred... Read More about Multi-label classification via incremental clustering on an evolving data stream..

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