Construções de comitês de classificadores multirrótulos no aprendizado semissupervisionado multidescrição

Data
2017-08-18
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Universidade Federal Rural do Semi-Árido

Resumo

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


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Citação
SILVA, Wilamis Kleiton Nunes da. Construções de comitês de classificadores multirrótulos no aprendizado semissupervisionado multidescrição. 2017. 108 f. Dissertação (Mestrado) - Curso de em Ciência da Computação, Universidade Federal Rural do Semi-Árido, Mossoró, 2017.