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Extensions of the k Nearest Neighbour methods for classification problems

The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a dataset. The results of our experiments using datasets from the UCI repository demonstrate that the classifiers produced perform generally better than the classic kNN and are more reliable, without being significantly slower.