BP<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN: <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Nearest Neighbor Classifier With Pairwise Distance Metrics and Belief Function Theory

BP<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN: <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Nearest Neighbor Classifier With Pairwise Distance Metrics and Belief Function Theory

The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms in pattern recognition field because it is very simple to understand but works quite well in practice. However, the performance of the kNN rule depends critically on its being given a good distance metric over the...

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Bibliographic Details
Main Authors: Lianmeng Jiao, Xiaojiao Geng, Quan Pan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Pattern classification
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-nearest-neighbor classifier
pairwise distance metric
belief function theory
Online Access:https://ieeexplore.ieee.org/document/8684242/

Internet

https://ieeexplore.ieee.org/document/8684242/

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