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2008

Extending Nearest Neighbor Classification with Spheres of Confidence

11 years 2 months ago
Extending Nearest Neighbor Classification with Spheres of Confidence
The standard kNN algorithm suffers from two major drawbacks: sensitivity to the parameter value k, i.e., the number of neighbors, and the use of k as a global constant that is independent of the particular region in which the example to be classified falls. Methods using weighted voting schemes only partly alleviate these problems, since they still involve choosing a fixed k. In this paper, a novel instance-based learner is introduced that does not require k as a parameter, but instead employs a flexible strategy for determining the number of neighbors to consider for the specific example to be classified, hence using a local instead of global k. A number of variants of the algorithm are evaluated on 18 datasets from the UCI repository. The novel algorithm in its basic form is shown to significantly outperform standard kNN with respect to accuracy, and an adapted version of the algorithm is shown to be clearly ahead with respect to the area under ROC curve. Similar to standard kNN, th...
Ulf Johansson, Henrik Boström, Rikard Kö
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where FLAIRS
Authors Ulf Johansson, Henrik Boström, Rikard König
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