Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

An Improved KNN Classification Algorithm based on Sampling

Authors
Zhiwei Cheng, Caisen Chen, Xuehuan Qiu, Huan Xie
Corresponding Author
Zhiwei Cheng
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.45How to use a DOI?
Keywords
KNN, classification algorithm, computational overhead, sampling.
Abstract
K nearest neighbor (KNN) algorithm has been widely used as a simple and effective classification algorithm. The traditional KNN classification algorithm will find k nearest neighbors, it is necessary to calculate the distance from the test sample to all training samples. When the training sample data is very large, it will produce a high computational overhead, resulting in a decline in classification speed. Therefore, we optimize the distance calculation of the KNN algorithm. Since KNN only considers the k samples of the shortest distance from the test sample to the nearest training sample point, the large distance training has no effect on the classification of the algorithm. The improved method is to sample the training data around the test data, which reduces the number of distance calculation of the test data to each training data, and reduces the time complexity of the algorithm. The experimental results show that the optimized KNN classification algorithm is superior to the traditional KNN algorithm.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/ammee-17.2017.45How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhiwei Cheng
AU  - Caisen Chen
AU  - Xuehuan Qiu
AU  - Huan Xie
PY  - 2017/06
DA  - 2017/06
TI  - An Improved KNN Classification Algorithm based on Sampling
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.45
DO  - https://doi.org/10.2991/ammee-17.2017.45
ID  - Cheng2017/06
ER  -