Canuto, Anne Magaly de PaulaRodrigues, Fillipe Morais2014-12-172014-11-052014-12-172014-02-21RODRIGUES, Fillipe Morais. Uso de confiabilidade na rotulação de exemplos em problemas de classificação multirrótulo com aprendizado semissupervisionado. 2014. 118 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal do Rio Grande do Norte, Natal, 2014.https://repositorio.ufrn.br/jspui/handle/123456789/18097The techniques of Machine Learning are applied in classification tasks to acquire knowledge through a set of data or information. Some learning methods proposed in literature are methods based on semissupervised learning; this is represented by small percentage of labeled data (supervised learning) combined with a quantity of label and non-labeled examples (unsupervised learning) during the training phase, which reduces, therefore, the need for a large quantity of labeled instances when only small dataset of labeled instances is available for training. A commom problem in semi-supervised learning is as random selection of instances, since most of paper use a random selection technique which can cause a negative impact. Much of machine learning methods treat single-label problems, in other words, problems where a given set of data are associated with a single class; however, through the requirement existent to classify data in a lot of domain, or more than one class, this classification as called multi-label classification. This work presents an experimental analysis of the results obtained using semissupervised learning in troubles of multi-label classification using reliability parameter as an aid in the classification data. Thus, the use of techniques of semissupervised learning and besides methods of multi-label classification, were essential to show the resultsapplication/pdfAcesso AbertoAprendizado de máquina. Aprendizado semissupervisionado. Classificação multirrótulo. Parâmetro de confiabilidadeMachine Learning. Semissupervised learning. Multi-label classification. Reliability ParametermasterThesisCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO