2004 | OriginalPaper | Buchkapitel
K Nearest Neighbor Classification with Local Induction of the Simple Value Difference Metric
verfasst von : Andrzej Skowron, Arkadiusz Wojna
Erschienen in: Rough Sets and Current Trends in Computing
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To induce both the global and the local metric we use the weighted Simple Value Difference Metric (SVDM). The experimental results show that the proposed classification model with local induction of a metric reduces classification error up to several times in comparison to the classical k-nn method.