Creating Prototypes for Fast Classification in Dempster-Shafer Clustering

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dc.creatorSchubert, Johan
dc.date2003-05-16
dc.date.accessioned2026-07-07T03:19:40Z
dc.date.available2026-07-07T03:19:40Z
dc.descriptionWe develop a classification method for incoming pieces of evidence in Dempster-Shafer theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces of evidence by comparing them with prototypes representing the clusters, instead of making a full clustering of all evidence. This method has a computational complexity of O(M * N) for each new piece of evidence, where M is the maximum number of subsets and N is the number of prototypes chosen for each subset. That is, a computational complexity independent of the total number of previously arrived pieces of evidence. The parameters M and N are typically fixed and domain dependent in any application.
dc.description11 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/cs/0305021
dc.identifierhttp://arxiv.org/abs/cs/0305021
dc.identifierin Qualitative and Quantitative Practical Reasoning, Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning (ECSQARU-FAPR'97), pp. 525-535, Bad Honnef, Germany, 9-12 June 1997, Springer-Verlag (LNAI 1244), Berlin, 1997
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31558
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.subjectI.2.3; I.5.3
dc.titleCreating Prototypes for Fast Classification in Dempster-Shafer Clustering
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