  This work proposes a new type of classifier called Morphological Classifier
(MC). MCs aggregate concepts from mathematical morphology and supervised
learning. The outcomes of this aggregation are classifiers that may preserve
shape characteristics of classes, subject to the choice of a stopping criterion
and structuring element. MCs are fundamentally based on set theory, and their
classification model can be a mathematical set itself. Two types of
morphological classifiers are proposed in the current work, namely,
Morphological k-NN (MkNN) and Morphological Dilation Classifier (MDC), which
demonstrate the feasibility of the approach. This work provides evidence
regarding the advantages of MCs, e.g., very fast classification times as well
as competitive accuracy rates. The performance of MkNN and MDC was tested using
p -dimensional datasets. MCs tied or outperformed 14 well established
classifiers in 5 out of 8 datasets. In all occasions, the obtained accuracies
were higher than the average accuracy obtained with all classifiers. Moreover,
the proposed implementations utilize the power of the Graphics Processing Units
(GPUs) to speed up processing.
