Canuto, Anne Magaly de PaulaDantas, Carine Azevedo2021-12-162021-12-162021-07-30DANTAS, Carine Azevedo. Uma análise de integração de técnicas de seleção dinâmica na construção de um sistema de classificação. 2021. 100f. Tese (Doutorado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2021.https://repositorio.ufrn.br/handle/123456789/45430The use of dynamic selection techniques, for attributes or members of an ensemble, has appeared in several works in the literature as a mechanism to increase the accuracy of rating ensembles. Individually, each of these techniques has already shown the benefits of using it. The objective of this work is to improve the efficiency of the classifier ensembles through the use of dynamic selection techniques for the definition of structure of these systems. With that, it will be possible to explore the use of these two techniques integrated in the classification of an instance, making each instance be classified using its own subset of attributes and classifiers. When used in an integrated manner, due to the use of the two dynamic processes, it is believed that the complete system has a long execution time. Aiming to overcome this disadvantage in its use, where the complete dynamic system will be used only in certain instances. Thus, some instances would be classified using all the dynamic system, while the other instances would be classified using only a single classifier. In other words, some instances may not require a level high complexity of the classification system. For these instances, a classifier will be used. In this way, the dynamic ensemble will only be used in instances considered difficult to classify. Initial results showed that the integration of these two dynamic techniques obtained promising results in terms of accuracy. Finally, these results were not significantly affected by the addition of the decision criterion, which generated a very significant reduction in the total processing time of the system.Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/ComputaçãoSeleção dinâmica de atributosSeleção dinâmica de classificadoresInstance HardnessdoctoralThesis