Like many other classifiers, k-NN classifier is noise-sensitive. Its accuracy highly depends on
the quality of the training data. Noise and mislabeled data, as well as outliers and overlaps …

The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction
technique. It summarizes the training data and generates representative prototypes. Its goal …

The efficiency of the k-Nearest Neighbour classifier depends on the size of the training set
as well as the level of noise in it. Large datasets with high level of noise lead to less accurate …

The k-Nearest Neighbor (k-NN) rule is widely used for classification tasks because of its
simplicity and efficiency. However, a well-known drawback of k-NN is its dependence on the …

Data reduction is a common preprocessing task in the context of the k nearest neighbour
classification. This paper presents WebDR, a web-based application where several data …

Although the k-NN classifier is considered to be an effective classification algorithm, it has
some major weaknesses that may render its use inappropriate for some application domains …