farknn
Farknn is a term used to describe a class of nearest-neighbor algorithms that incorporate feature-aware weighting and robust distance metrics to improve performance on noisy or high-dimensional datasets. The name is sometimes described as “feature-adjusted robust k-nearest neighbors,” though exact naming and implementations vary across projects.
Typically farknn methods begin with data preprocessing such as normalization and, if needed, missing-value handling. The
Farknn supports multiclass classification and regression and is applied across domains including pattern recognition, image or
Limitations include higher computational cost and memory usage relative to plain k-NN, as well as sensitivity
See also: k-nearest neighbors, metric learning, weighted distance, instance-based learning.