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.

Citation

V.T. Tsoukalas, V.G. Kaburlasos, C. Skourlas, “A granular, parametric KNN classifier”, 17th Panhellenic Conference on Informatics (PCI 2013), Thessaloniki, Greece, 19-21 September 2013, pp. 319-326.

View on ResearchGate