Utilizando aprendizado emissupervisionado multidescrição em problemas de classificação hierárquica multirrótulo

• Ano de defesa: 2017
• Autor(a) principal: Araújo, Hiury Nogueira de
• 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 semissupervisionado; Classificação hierárquica; Multirrótulo; Co-training; Self-training; Semi-supervised learning; Hierarchical multi-label classification; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufersa.edu.br/handle/prefix/830

Data classification is a task applied in various areas of knowledge, therefore, the focus of ongoing research. Data classification can be divided according to the available data, which are labeled or not labeled. One approach has proven very effective when working with data sets containing labeled and unlabeled data, this called semi-supervised learning, your objective is to label the unlabeled data by using the amount of labeled data in the data set, improving their success rate. Such data can be classified with more than one label, known as multi-label classification. Furthermore, these data can be organized hierarchically, thus containing a relation therebetween, this called hierarchical classification. This work proposes the use of multi-view semi-supervised learning, which is one of the semissupervisionado learning aspects, in problems of hierarchical multi-label classification, with the objective of investigating whether semi-supervised learning is an appropriate approach to solve the problem of low dimensionality of data. An experimental analysis of the methods found that supervised learning had a better performance than semi-supervised approaches, however, semi-supervised learning may be a widely used approach, because, there is plenty to be contributed in this area