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Dynamic self-organising swarm for unsupervised prototype generation

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conference contribution
posted on 2021-01-18, 05:57 authored by Su NguyenSu Nguyen, Binh TranBinh Tran, Damminda AlahakoonDamminda Alahakoon
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Growing big data has posed a great challenge for machine learning algorithms. To cope with big data, the algorithm has to be both efficient and accurate. Although evolutionary computation has been successfully applied to many complex machine learning tasks, its ability to handle big data is limited. In this paper, we proposed a dynamic self-organising swarm algorithm to learn an effective set of prototypes for big high-dimensional datasets in an unsupervised manner. The novelties of this new algorithm are the energy-based fitness function, the adaptive topological neighbourhood, the growing/shrinking capability, and the efficient learning scheme. Experiments with well-known datasets show that the proposed algorithm can maintain a very compact set of prototypes and achieve competitive predictive performance as compared to other algorithms in the literature. The analyses also show that prototypes generated by the proposed algorithms have a stronger separatability compared to those from other prototype generation algorithms.


Publication Date



2020 IEEE Congress on Evolutionary Computation (CEC). 2020 Conference Proceedings.



Place of publication

Piscataway, USA





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IEEE Congress on Evolutionary Computation


Glasgow, UK

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