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2006

Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence

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Neighborhood size selection in the k-nearest-neighbor rule using statistical confidence
The k-nearest-neighbor rule is one of the most attractive pattern classification algorithms. In practice, the choice of k is determined by the cross-validation method. In this work, we propose a new method for neighborhood size selection that is based on the concept of statistical confidence. We define the confidence associated with a decision that is made by the majority rule from a finite number of observations and use it as a criterion to determine the number of nearest neighbors needed. The new algorithm is tested on several real-world datasets and yields results comparable to the k-nearest-neighbor rule. However, in contrast to the k-nearest-neighbor rule that uses a fixed number of nearest neighbors throughout the feature space, our method locally adjusts the number of nearest neighbors until a satisfactory level of confidence is reached. In addition, the statistical confidence provides a natural way to balance the trade-off between the reject rate and the error rate by excludin...
Jigang Wang, Predrag Neskovic, Leon N. Cooper
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Jigang Wang, Predrag Neskovic, Leon N. Cooper
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