Morphological classifiers
This work introduces a novel classifier type that could benefit domains requiring fast and accurate shape-based classification, though it appears incremental as it builds on existing morphological concepts.
The authors tackled the problem of classification by proposing Morphological Classifiers (MCs), which combine mathematical morphology and supervised learning to preserve shape characteristics, resulting in competitive accuracy and very fast classification times, tying or outperforming 14 established classifiers in 5 out of 8 datasets.
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.