K Nearest Neighbor Equality: Giving equal chance to all existing classes |
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Authors: | B Sierra E Lazkano I Irigoien E Jauregi I Mendialdua |
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Affiliation: | Department of Computer Science and Artificial Intelligence, University of the Basque Country, Spain |
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Abstract: | The nearest neighbor classification method assigns an unclassified point to the class of the nearest case of a set of previously classified points. This rule is independent of the underlying joint distribution of the sample points and their classifications. An extension to this approach is the k-NN method, in which the classification of the unclassified point is made by following a voting criteria within the k nearest points.The method we present here extends the k-NN idea, searching in each class for the k nearest points to the unclassified point, and classifying it in the class which minimizes the mean distance between the unclassified point and the k nearest points within each class. As all classes can take part in the final selection process, we have called the new approach k Nearest Neighbor Equality (k-NNE).Experimental results we obtained empirically show the suitability of the k-NNE algorithm, and its effectiveness suggests that it could be added to the current list of distance based classifiers. |
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Keywords: | Nearest neighbor Supervised classification Machine Learning Non-parametric pattern recognition |
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