Proper Understanding of Condensed Nearest Neighbor
Condensed Nearest Neighbors algorithm helps to reduce the dataset X for k-NN classification. It constructs a subset of examples which are able to correctly classify the original data set using a 1-NN algorithm.

It is returning not the array of misclassified points, but a subset Z of the data set X. 

CNN works like that:

1) Scan all elements of X, looking for an element x whose nearest prototype from Z has a different label than x

2) Remove x from X and add it to Z

3) Repeat the scan until no more prototypes are added to Z

Z used instead of X for kNN classification. 

An advantage of this method is decreasing of execution time, reducing a space complexity