Vassilis Th. Tsoukalas
ABSTRACT
This work presents a granular K Nearest Neighbor, or grKNN for short, classifier in the metric lattice of Intervals' Numbers (INs). An IN here represents a population of numeric data samples. We detail how the grKNN classifier can be parameterized towards optimizing it. The capacity of a preliminary grKNN classifier is demonstrated, comparatively, in four benchmark classification problems. The far-reaching potential of the proposed classification scheme is discussed.
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Index Terms
- A granular, parametric KNN classifier
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