A direct boosting algorithm for the k-nearest neighbor classifier via local warping of the distance metric |
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Authors: | Toh Koon Charlie Neo Dan Ventura |
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Affiliation: | Department of Computer Science, Brigham Young University, Provo, UT 84602, USA |
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Abstract: | Though the k-nearest neighbor (k-NN) pattern classifier is an effective learning algorithm, it can result in large model sizes. To compensate, a number of variant algorithms have been developed that condense the model size of the k-NN classifier at the expense of accuracy. To increase the accuracy of these condensed models, 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. |
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Keywords: | Classification kNN Boosting Distance measures |
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