Nearest Neighbor enhancements

keith

Hi,

I would like to see NearestNeighbor enhanced in the following ways:

1) When computing the nearest neighbor for a given point, optionally exclude points where distance = 0 (most often, this will be just itself).  As it is now, if I build a KNN model on a dataset with K=1, and then apply the model to the dataset, the predictions are perfect since the nearest neighbor of each point is itself.  This is similar to what Weka's LinearNNSearch option -S does (although the other nearest neighbor algorithms Weka supports unfortunately don't have this option).

2) Be able to specify the weighting kernel function, rather than just have a toggle for"weighted_vote".  This would bring it up to the same capabilities as W-LWL, in which can specify linear, Epanechnikov, tricube, inverse, or gaussian weights.

3) Ability to build a full local polynomial regression (aka loess) model, similar to what locfit does in R.

IngoRM

Hi Keith,

thanks for sending those suggestions in. Some are easier to implement, others will of course need more time. However, I have added all points to our Todo list.

Cheers,
Ingo