Classificação multirrótulo com aprendizado semissupervisionado: uma análise multivisão de dados

• Ano de defesa: 2016
• Autor(a) principal: Assis, Mateus Silvério de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
• Palavras-chave em Português: Classificação multirrótulo; Aprendizado semissupervisionado; Multivisão de dados; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/22078

In the the last years, the computational techniques used for machine learning have been divided or categorized according to the degree of supervision that exists in these training’s set and according on the number of labels in this class attribute. Within these divisions, we find the semi-supervised learning, a technique that works well when nor all labels examples of the training set are known. In the other hand, the multi-label classification also is present in these categories and it proposes to classify examples when they are associated with one or more labels. The combination of these learning techniques generates the classification semi-supervised multi-label. Also in this context, there are sides that work with the semi-supervised learning for single vision and semisupervised learning data for multiple viewing data. The semi-supervised learning algorithms for multiple viewing data has the basic idea of the exploitation of disagreements between the predictions of different classifiers, which is a subject rarely addressed in research. Thus, this work proposes the use of semi-supervised learning for multi-label classification using an approach with multiple viewing data, showing the results of some experiments and comparing some results of experiments using the new methods with the results of experiments using existing methods.