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Dynamic self-organising swarm for unsupervised prototype generation

conference contribution
posted on 18.01.2021, 05:57 by Phan Nguyen, Binh Tran, Lakpriya 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.
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Publication Date

01/08/2020

Proceedings

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

Publisher

IEEE

Place of publication

Piscataway, USA

Pagination

8p.

ISBN-13

9781728169293

Name of conference

IEEE Congress on Evolutionary Computation

Location

Glasgow, UK

Starting Date

19/07/2020

Finshing Date

24/07/2020

Rights Statement

The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

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