A neural network and iterative optimization hybrid for Dempster-Shafer clustering

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dc.creatorSchubert, Johan
dc.date2003-05-16
dc.date.accessioned2026-07-07T03:19:41Z
dc.date.available2026-07-07T03:19:41Z
dc.descriptionIn this paper we extend an earlier result within Dempster-Shafer theory ["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 98)] where a large number of pieces of evidence are clustered into subsets by a neural network structure. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. While the neural method had a much lower computation time than iterative optimization its average clustering performance was not as good. Here, we develop a hybrid of the two methods. We let the neural structure do the initial clustering in order to achieve a high computational performance. Its solution is fed as the initial state to the iterative optimization in order to improve the clustering performance.
dc.description8 pages, 10 figures
dc.identifierhttps://arxiv.org/abs/cs/0305024
dc.identifierhttp://arxiv.org/abs/cs/0305024
dc.identifierin Proceedings of EuroFusion98 International Conference on Data Fusion (EF'98), M. Bedworth, J. O'Brien (Eds.), pp. 29-36, Great Malvern, UK, 6-7 October 1998
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31561
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.subjectI.2.3; I.2.6; I.5.3
dc.titleA neural network and iterative optimization hybrid for Dempster-Shafer clustering
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