![](bg7.png)
possible to verify the generalization capacity. The
classification model evaluated in this work was shown
to be efficient, obtaining an average of 94.35 % ac-
curacy. Being able to reach a high accuracy, even
with the unbalanced dataset and with the iterations
obtained in the cross validation being proxies.
ACKNOWLEDGMENTS
The elaboration of this work would not have been
possible without the collaboration of the Engineering
and DecisionSupport Research Center (GECAD) of
the Institute of Engineering, Polytechnic Institute of
Porto, Portugal and FAPEMA.
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