Dwulit’s Hull as Means of Optimization of kNN Algorithm
The paper includes a description of a novel method for reducing the size of a training set in order to reduce memory requirements and classification complexity. Our method allows the condensing of the training set in a way that it is both training set consistent (classifies all training data points correctly) and decision-boundary consistent (the decision boundary does not changes after applying our method) for NN classifiers. Furthermore, the algorithm described here allows the utilization of a parallel computing paradigm in order to increase performance.
KeywordsConvex Hull Voronoi Diagram Near Neighbor Delaunay Triangulation Decision Boundary
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