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dc.creatorRodrigues, Fillipe
dc.creatorSantos, Araken
dc.creatorCanuto, Anne
dc.date.accessioned2017-06-21T19:40:33Z
dc.date.available2017-06-22
dc.date.available2017-06-21T19:40:33Z
dc.date.issued2014-07-11
dc.identifier.urihttp://memoria.ifrn.edu.br/handle/1044/1182
dc.languageengpt_BR
dc.publisherInstituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Nortept_BR
dc.relation.ispartofIJCNN - International Joint Conference on Neural Networkspt_BR
dc.rightsAcesso Restritopt_BR
dc.subjectMulti-labelpt_BR
dc.subjectSemi-supervisedpt_BR
dc.subjectArtificial Intelligencept_BR
dc.titleConfidence factor and feature selection for semi-supervised multi-label classification methodspt_BR
dc.title.alternativeFator de confidência em seleção de características para métodos de classificação semi-supervisionado multi-rótulopt_BR
dc.typeArtigo de Periódicopt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentParnamirimpt_BR
dc.publisher.initialsIFRNpt_BR
dc.subject.cnpqCiência da Computaçãopt_BR
dc.subject.cnpqInteligência Artificialpt_BR
dc.citation.issue2014pt_BR
dc.description.resumoIn this paper, we investigate two important problems in multi-label classification algorithms, which are: the number of labeled instances and the high dimensionality of the labeled instances. In the literature, we can find several papers about multi-label classification problems, where an instance can be associated with more than one label simultaneously. One of the main issues with multi-label classification methods is that many of these require a high number of instances to be able to generalize in an efficient way. In order to solve this problem, we used semi-supervised learning, which combines labeled and unlabeled instances during the training process. In this sense, the semi-supervised learning may become an essential tool to define, efficiently, the process of automatic assignment of labels. Therefore, this paper presents four semi-supervised methods for the multilabel classification, focusing on the use of a confidence parameter in the process of automatic assignment of labels. In order to validate the feasibility of these methods, an empirical analysis will be conducted using high-dimensional datasets, aiming to evaluate the performance of such methods in different situations. In this case, we will apply a feature selection algorithm in order to reduce, in an efficient way, the number of features to be used by the classification methods.pt_BR


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