Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/11082
Título: Classificação de dados sensoriais de cafés especiais com resposta multiclasse via Algoritmo Boosting e Bagging
Título(s) alternativo(s): Classification of sensory data special coffees with answer multiclass through Algorithm Boosting and Bagging
Autores: Menezes, Fortunato Silva de
Cirillo, Marcelo Ângelo
Brighenti, Carla Regina Guimarães
Scalon, João Domingos
Cirillo, Marcelo Ângelo
Palavras-chave: Métodos de classificação
Café - Qualidade
Análise Discriminante
Classification methods
Coffee - Quality
Discriminant Analysis
Data do documento: 26-Abr-2016
Editor: Universidade Federal de Lavras
Citação: OLIVEIRA, L. M. de. Classificação de dados sensoriais de cafés especiais com resposta multiclasse via Algoritmo Boosting e Bagging. 2016. 85 p. Dissertação (Mestrado em Estatística e Experimentação Agropecuária)-Universidade Federal de Lavras, Lavras, 2016.
Resumo: Automatic classification methods have been developed in machine learning area in order to facilitate the categorization of data. Among the most successful methods include the Boosting and Bagging. The Bagging works by combining classifiers adjusted in bootstrap samples of the data and the Boosting works by applying sequentially an algorithm to rank the reweighted versions of the set of training data, giving greater weight to the observations misclassified in the previous step. These classifiers are characterized by providing satisfactory results, low computational cost and benefit of implementation simplicity. Given these characteristics, comes an interest in checking the performance of these automated methods compared with traditional existing classification methods in Statistics, Linear Discriminate Analysis and Quadratic. In order to compare these techniques it was used misclassification rates and accuracy of the models. To improve confidence in the use of Boosting and Bagging methods in more complex problems of classification, a study was carried out by applying these techniques in real and simulated data composed of more than two categories in the response variable. In this dissertation, to encourage the implementation of Boosting and Bagging was held in an application Sensory Analysis. We conclude that automatic methods have a good classification performance by providing lower error rates than Discriminant Linear analysis and Quadratic Discriminant analysis in the tested applications.
URI: http://repositorio.ufla.br/jspui/handle/1/11082
Aparece nas coleções:Estatística e Experimentação Agropecuária - Mestrado (Dissertações)



Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.