Using k-nearest-neighbor classification in the leaves of a tree

Authors:
Samuel E. Buttrey;Ciril Karo
Affiliations:
Department of Operational Research OR/Sb, Naval Postgraduate School, Monterey CA;Department of Operational Research OR/Sb, Naval Postgraduate School, Monterey CA
Venue:
Computational Statistics & Data Analysis
Year:
2002

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Abstract

We construct a hybrid (composite) classifier by combining two classifiers in common use--classification trees and k-nearest-neighbor (k-NN). 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.