Prototype Selection
Backgrond kNN prototype selection Summary List
There are couple drawbacks for KNN
high storage for data computation for decision boundary intolerance to noise There are couple methods address above issue
better similarity metric or better distance function k-d trees or R-trees as storage reduction technique (prototype selection) Prototype Selection 1. edition method - remove noise 1. condensation method - remove superfluous dataset 1. hybrid method - achive elimination of noise and superfluous at the same time……