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		<doi>10.1109/SIBGRAPI.2005.19</doi>
		<citationkey>BrevePontMasc:2005:CoMeSt</citationkey>
		<title>Combining methods to stabilize and increase performance of neural network-based classifiers</title>
		<format>On-line</format>
		<year>2005</year>
		<numberoffiles>1</numberoffiles>
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		<author>Breve, Fabricio Aparecido,</author>
		<author>Ponti Junior, Moacir Pereira,</author>
		<author>Mascarenhas, Nelson Delfino d'Ávila,</author>
		<affiliation>Departamento de Computação – Universidade Federal de São Carlos, São Paulo, SP, Brasil,</affiliation>
		<editor>Rodrigues, Maria Andréia Formico,</editor>
		<editor>Frery, Alejandro César,</editor>
		<e-mailaddress>fbreve@gmail.com</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)</conferencename>
		<conferencelocation>Natal, RN, Brazil</conferencelocation>
		<date>9-12 Oct. 2005</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>classifier combining neural networks multilayer perceptron dempster-shafer decision templates bagging pattern recognition soil science tomography.</keywords>
		<abstract>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.</abstract>
		<language>en</language>
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