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A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric

by Neo, TohKoon 1977-

Abstract (Summary)
The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is therefore desirable for increasing the accuracy of these condensed models. Unfortunately, there does not exist a boosting algorithm that works well with k-NN directly. We present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting. An empirical study conducted on 10 standard databases from the UCI repository shows that this new Boosted k-NN algorithm has increased generalization accuracy in the majority of the datasets and never performs worse than standard k-NN.
Bibliographical Information:

Advisor:

School:Brigham Young University

School Location:USA - Utah

Source Type:Master's Thesis

Keywords:computer science machine learning k nearest neighbor knn boosting

ISBN:

Date of Publication:11/16/2007

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