Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/564720
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dc.contributor.authorPRADO, H. A. do
dc.contributor.authorENGEL, P. M.
dc.contributor.authorSILVA, K. C. da
dc.date.accessioned2025-04-30T05:50:24Z-
dc.date.available2025-04-30T05:50:24Z-
dc.date.created2002-04-22
dc.date.issued2001
dc.identifier.citationIn: SIMPOSIO ARGENTINO EN INTELIGENCIA ARTIFICIAL - ASAI'2001; JORNADAS ARGENTINAS DE INFORMATICA E INVESTIGACION OPERATIVA, 30., 2001, Buenos Aires. Anales JAIIO. Buenos Aires: Sociedad Argentina de Informatica e Investigacion Operativa, 2001.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/564720-
dc.descriptionDuring the last years much effort has been devoted to generate knowledge through Data Mining techniques. Despite of all advances, few efforts have been addressed in cope with post processing problems. This paper is about those problems in Combinatorial Neural Model (CNM). CNM, a supervised learning algorithm introduced by Machado, received many improvements that made it useful for Data Mining. Two main problems arc approached. The first one corresponds 1o the conflicts that can emerge in the knowledge base since the model acquires knowledge from Different sources, cither specialists or examples. In this way, we apply the concept of extended negation, as the Boolean negation is not so natural. The second problem arises after applying the pruning process to CNM. Since the model is incremental, any part of the knowledge base pruned after the training process in time t, can be important to the training process in time. Disregarding this pruned part can Lend to loss of knowledge. Considering that it is not possible to avoid pruning, and thus to maintain the knowledge base untouched during all its lifetime, we propose an approach to mitigate the problem.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectInteligencia artificial
dc.subjectMineração de dados
dc.subjectRedes neurais
dc.titleDealing with inconsistencies and knowledge loss in combinatioral neural model.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroBase de Dados
dc.subject.thesagroPrograma de Computador
dc.subject.thesagroTecnologia da Informação
dc.subject.thesagroInformática
dc.format.extent2p. 74-84.
riaa.ainfo.id564720
riaa.ainfo.lastupdate2025-04-29
dc.contributor.institutionHERCULES ANTONIO DO PRADO, CPAC; PAULO MARTINS ENGEL, UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL; KATIA CILENE DA SILVA, UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL.
Appears in Collections:Artigo em anais de congresso (CPAC)

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