A comparative analysis of attribute reduction algorithms applied to wet-blue leather defects classification.
AUTOR(ES)
AMORIM, W. P.
FONTE
BRASILIAN SYMPOSIUM ON COMPUTER GRAPHICS AND IMAGE PROCESSING
DATA DE PUBLICAÇÃO
2011
RESUMO
This paper presents an attribute reduction comparative study on four linear discriminant analysis techniques: FisherFace, CLDA, DLDA and YLDA. The attribute reduction has been applied to the problem of leather defect c1assification using four different c1assifiers: C4.5, KNN, Naive Bayes and Support Veetor Machines. Results and analyses on the performance of correct c1assification rates as the number of attributes were reduced are reported.
ASSUNTO(S)
ACESSO AO ARTIGO
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