Comparison of Distance Measurement Methods on K-Nearest Neighbor Algorithm For Classification

DOI

10.2991/aisr.k.200424.054How to use a DOI?

Keywords

Distance measurement, K-Nearest Neighbor, Euclidean Distance, Manhattan Distance, Tchebychev Distance, Cosine Distance

Abstract

K-Nearest Neighbor is a non-parametric classification algorithm that does not use training data and initial assumptions or models in the calculation process. The quality of the k-Nearest Neighbor classification results is very dependent on distance between object and value of k specified, so the selection for distance measurement method determines the results of classification. This study compares several distance measurement method, including Euclidean distance, Manhattan distance, Tchebychev distance and Cosine distance to see which distance measurement method can work optimally on the k-Nearest Neighbor algorithm. The selection of k values also determines the results of k-Nearest Neighbor classification algorithm, so determining the k value also needs to be considered. The data used in this study is a dataset of cervical cancer. The highest accuracy results obtained using the Cosine distance measurement method that is equal to 92.559% with a value of k = 9. Based on the accuracy values that have been compared, the most optimal distance measurement method is Cosine distance with the best k value obtained is k = 9 even though this distance measurement method has the highest computing time which is equal to 0.898 seconds.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Taca ROSA
AU  - Rifkie PRIMARTHA
AU  - Adi WIJAYA
PY  - 2020
DA  - 2020/05/06
TI  - Comparison of Distance Measurement Methods on K-Nearest Neighbor Algorithm For Classification
BT  - Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019)
PB  - Atlantis Press
SP  - 358
EP  - 361
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.200424.054
DO  - 10.2991/aisr.k.200424.054
ID  - ROSA2020
ER  -