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The Internet of Things (IoT) is characterized by a broad range of resources connected to the Internet, requesting and providing services simultaneously. Given this scenario, suitably selecting the resources that best meet users' demands has been a relevant and current research challenge. This paper presents the EXEHDA-RR, a proposal to classify and select the most appropriate resource, applying fuzzy logic to solve uncertainties in the definition of ideal weights for QoS attributes, and adding machine learning to the preclassification of resources in order to reduce the computational cost generated by the MCDA algorithms. The experimental results of the pre-classification show the efficiency of the proposed model. * O presente trabalho foi realizado com apoio da CAPES (Programa Nacional de Cooperação Acadêmica -Procad) e da FAPERGS (Programa Pesquisador Gaúcho -PqG).

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