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Hybrid k -Nearest Neighbor Classifier.

IEEE transactions on cybernetics, 2017
Conventional k -nearest neighbor (KNN) classification approaches have several limitations when dealing with some problems caused by the special datasets, such as the sparse problem, the imbalance problem, and the noise problem. In this paper, we first perform a brief survey on the recent progress of the KNN classification approaches.
Zhiwen, Yu   +5 more
openaire   +1 more source

Ensemble k-nearest neighbors based on centroid displacement

Information Sciences, 2023
Alex X. Wang, Binh P Nguyen
exaly  

K-Nearest Neighbor

2012
Xiaowei Huang, Gaojie Jin, Wenjie Ruan
openaire   +1 more source

High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis

Electronics (Switzerland), 2023
Elife Ozturk Kiyak   +2 more
exaly  

K-Nearest Neighbors

2019
Brad Boehmke, Brandon Greenwell
openaire   +1 more source

Fusing absolute and relative information for augmenting the method of nearest neighbors for ordinal classification

Information Fusion, 2020
Mengzi Tang   +2 more
exaly  

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