%0 Conference Proceedings
%4 dpi.inpe.br/plutao/2012/11.28.14.50.59
%2 dpi.inpe.br/plutao/2012/11.28.14.51
%@doi 10.1109/FUZZ-IEEE.2012.6251149
%@isbn 978-146731506-7
%@issn 10987584
%F lattes: 8073731810160654 2 SandriMendMart:2012:CaSt
%T Weighted Fuzzy Similarity Relations Case-Based Reasoning: a Case Study
%D 2012
%A Sandri, Sandra Aparecida,
%A Mendonça, Jonas Henrique,
%A Martins-Bedê, Flávia,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress
%@electronicmailaddress jonas.henrique01@gmail.com
%B IEEE International Conference on Fuzzy Systems.
%C Brisbane
%8 10-15 June
%I IEEE
%V Article number6251149
%S Proceedings
%K Basic principles, CBr, Classification tasks, Fuzzy similarity relation, Gradual rules, Implication operators, Prevalence estimation, Problem description, Training strategy.
%X This paper describes a fuzzy similarity relation approach to Case-Based Reasoning. Residuated implication operators are used to create a fuzzy resemblance relation between cases, modeling the CBR basic principle the more similar the problem descriptions are, "the more similar the solution descriptions are" as a fuzzy gradual rule. We take the classification of Schistosomiasis prevalence estimation in a region of Brazil as case study, in order to investigate the effects in such a framework of weighting cases individually in classification tasks, considering a set of training strategies.
%@language en