%0 Conference Proceedings
%4 sid.inpe.br/banon/2005/07.07.18.38
%2 sid.inpe.br/banon/2005/07.07.18.38.42
%@doi 10.1109/SIBGRAPI.2005.19
%T Combining methods to stabilize and increase performance of neural network-based classifiers
%D 2005
%A Breve, Fabricio Aparecido,
%A Ponti Junior, Moacir Pereira,
%A Mascarenhas, Nelson Delfino d'Ávila,
%@affiliation Departamento de Computação – Universidade Federal de São Carlos, São Paulo, SP, Brasil,
%E Rodrigues, Maria Andréia Formico,
%E Frery, Alejandro César,
%B Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)
%C Natal, RN, Brazil
%8 9-12 Oct. 2005
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K classifier combining neural networks multilayer perceptron dempster-shafer decision templates bagging pattern recognition soil science tomography.
%X In this paper we present a set of experiments in order to recognize materials in multispectral images, which were obtained with a tomograph scanner. These images were classified by a neural network based classifier (Multilayer Perceptron) and classifier combining techniques (Bagging, Decision Templates and Dempster-Shafer) were investigated. We also present a performance comparison between the individual classifiers and the combiners. The results were evaluated by the estimated error (obtained using the Hold-Out technique) and the Kappa coefficient, and they showed performance stabilization.
%@language en
%3 fbreve_combining.pdf