Bootstrap agregating : an investigation of performance in statistics and neural networks classifiers, numerical evaluation and application on breast cancer diagnostic support. / Agregação via bootstrap: uma investigação de desempenho em classificadores estatísticos e redes neurais, avaliação numérica e aplicação no suporte ao diagnóstico de câncer de mama .

AUTOR(ES)
DATA DE PUBLICAÇÃO

2007

RESUMO

In pattern recognition, the medical diagnosis has received great attention. In gene-ral, the emphasis has been to identify one best model for diagnostic forecast, measured according to generalization ability. In this context, ensembles methods have been eficients, can be considered on the improvement of performance in diagnostic tasks that demand greater precision. The bagging method, purposed from Breiman (1996), uses bootstrap to generate different samples of the training set, building classifiers with the generated samples and combining different forecasts for majority vote. In general, empirical estudies are done for evaluate the bagging performance. In this thesis, we investigate the bagging generalization ability for statistical usual classifiers and the multilayer perceptron net through sthocastic simulation. Different structures of separation of populations are build from especific distributions. Additionally, we make an application on diagnostic suport of brest cancer. The results were obtained using R. In general, we observed that bagging performance depends on the population separation behavior. In the application, bagging showed to be ecient on sensibility improvement.

ASSUNTO(S)

rede neural bagging breast cancer classificação estatística bootstrap reconhecimento de padrões exatas e da terra câncer de mama biometria

ACESSO AO ARTIGO

Documentos Relacionados