document: Using k-nearest-neighbor classification in the leaves of a tree We construct a hybrid -LRB- composite -RRB- classifier by combining two classifiers in common use - classification trees and k-nearest-neighbor -LRB- k-NN -RRB- . In our scheme we divide the feature space up by a classification tree , and then classify test set items using the k-NN rule just among those training items in the same leaf as the test item . This reduces somewhat the computational load associated with k-NN , and it produces a classification rule that performs better than either trees or the usual k-NN in a number of well-known data sets
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I
extractive_keyphrases: classification trees computational load data sets
abstractive_keyphrases: k-nearest-neighbor classification tree leaves hybrid composite classifier feature space division k-nn rule