%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@archivingpolicy denypublisher denyfinaldraft24
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%@resumeid 8JMKD3MGP5W/3C9JHTU
%@usergroup administrator
%@usergroup simone
%3 strategies for improving.pdf
%X One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.
%8 Oct.
%N 1
%T Strategies for improving the modeling and interpretability of Bayesian networks
%@secondarytype PRE PI
%K Knowledge discovery, Markov chains, Bayesian networks, Multivariate regression.
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%@group
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%@group
%@group LAC-INPE-MCT-BR
%@group LAC-INPE-MCT-BR
%@secondarykey INPE-14762-PRE/9733
%@copyholder SID/SCD
%@issn 0169-023X
%2 sid.inpe.br/mtc-m17@80/2006/12.04.13.25.19
%@affiliation UFPA
%@affiliation UFPA
%@affiliation Universidade da Amazônia
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation UFPA
%@affiliation UFPA
%B Data and Knowledge Engineering
%P 91-107
%4 sid.inpe.br/mtc-m17@80/2006/12.04.13.25
%D 2007
%V 63
%A Santana, Ádamo L.,
%A Francês, Carlos R.,
%A Rocha, Cláudio A.,
%A Carvalho, Solon Venâncio de,
%A Vijaykumar, Nandamudi Lankalapalli,
%A Rego, Liviane P.,
%A Costa, João C.,
%@dissemination PORTALCAPES
%@area COMP