The k Nearest Neighbors (KNN) algorithm has been widely applied in various supervised
learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision …

While fast and accurate data classification techniques are vital to many applications, K-
Nearest Neighbor algorithm (KNN) is considered the most important algorithm used in data …

As the healthcare industry becomes more reliant upon electronic records, the amount of
medical data available for analysis increases exponentially. While this information contains …

The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …

Abstract The k Nearest Neighbor (KNN) algorithm has been widely applied in various
supervised learning tasks due to its simplicity and effectiveness. However, the quality of …

In this work, we study the performance of the K-Nearest Neighbour (KNN) based predictive
model in sequential as well as parallel mode to observe its performance both in terms of …

Spatial cross-matching operation over geospatial polygonal datasets is important to a variety
of GIS applications. However, it involves extensive computation cost associated with …

KNN has the reputation to be the word simplest but efficient supervised learning algorithm
used for either classification or regression. KNN prediction efficiency highly depends on the …

One of the widely used classification algorithms is k-Nearest Neighbours (k-NN). Its
popularity is mainly due to its simplicity, effectiveness, ease of implementation and ability to …

The k-nearest neighbors algorithm (kNN) is renowned for solving classification tasks. The
notion of kNN is to seek similar data instances in the dataset as prediction reference, for …