2.2.1. K-Nearest Neighbors

This classification algorithm is fundamentally simple but exhibits relative high performance. The underlying intuition is based on classifying the information from specific features from the categories of its closest neighbors [46]. The number of neighbors (*k*) can be user-defined or it can vary depending on the local density of the neighborhood. Likewise, to quantify the proximity of such neighbors, different measures of distance can be used, such as Euclidean or Manhattan distance. The distance from a close neighbor can also be weighted so that it provides a higher influence than a farther one.

Major limitations of this algorithm are its lack of performance when dealing with highdimensional data and its high prediction times for large datasets. The reader is referred to [47] for a deeper description of this algorithm.
