Construções de comitês de classificadores multirrótulos no aprendizado semissupervisionado multidescrição

• Ano de defesa: 2017
• Autor(a) principal: Silva, Wilamis Kleiton Nunes da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal Rural do Semi-Árido; Brasil; UFERSA; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Aprendizado de máquina; Aprendizado semissupervisionado multidescrição; Classificação multirrótulo; Comitês de classificadores; Machine learning; Multi-description semi supervised learning; Multi-label classification; Classification committees; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufersa.edu.br/handle/tede/812

Multi-label problems have become increasingly common, for a label can be attributed to more than one instance, being called multi-label classification problems. Among the di_erent multilabel classification methods we can mention: BR (Binary Relevance), LP (Label Powerset) And RAkEL (RAndom k labELsets). Such methods have been recognized as methods for transforming the Problem, since they consist of turning the multi-label problem into several problems of traditional classification (mono label). However, the adoption of Classificatory committees in multi-label classification problems has still been new-found so far, With a great field to be explored for conducting researches as well. This work aims of doing a study on the construction of multilabel classifiers committees Built through the application of multi- description semisupervised learning techniques, in order to verify if application of this type of learning in the construction of committees results in improvements linked to the results. The committees of classifiers used in the experiments were Bagging, Boosting and Stacking as methods of transformation of the problems used were the BR, LP and Rakel methods and for classification multi-label multi-label semi-supervised multi-description was used Co-Training. At the end of the experimental analyzes, it was verified that the use of the semi-supervised approach presented satisfactory results, since the two approaches presented similar results