k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for ...

False nearest neighbors (FNN) method is examinated with respect to equivariance of individual observables. The aim is to reveal the most appropriate observable for phase space reconstruction. Results calculated for benchmark systems are compared with symbolic observability degrees. The FNN method resulted in different values of embedding dimensions when calculated for various observables of the...

K-Nearest Neighbor Classification (kNNC) makes the classification by getting votes of the k-Nearest Neighbors. Performance of kNNC is depended largely upon the efficient selection of k-Nearest Neighbors. All the attributes describing an instance does not have same importance in selecting the nearest neighbors. In real world, influence of the different attributes on the classification keeps on c...

Nearest Neighbor search is an important and widely used problem in a number of important application domains. In many of these domains, the dimensionality of the data representation is often very high. Recent theoretical results have shown that the concept of proximity or nearest neighbors may not be very meaningful for the high dimensional case. Therefore, it is often a complex problem to find...