%0 Conference Proceedings
%T Combining methods to stabilize and increase performance of neural network-based classifiers
%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)
%8 9-12 Oct. 2005
%I IEEE Computer Society
%J Los Alamitos
%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.