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An Improved K-nearest Neighbor Algorithm Using Tree Structure and Pruning Technology

Juan Li

School of Distance Education, Shaanxi Normal University, 710062 Xi’an, China

* Corresponding Author: Juan Li, email

Intelligent Automation & Soft Computing 2019, 25(1), 35-48.


K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing algorithms. The results indicate that the proposed algorithm can obtain higher classification efficiency and smaller search reference set on UCI datasets. Furthermore, the proposed algorithm can overcome the shortcoming of KNN and improve the performance of multi-class and unbalanced classification. This illustrates that the proposed algorithm is an expedient method in design nearest neighbour classifiers.


Cite This Article

J. Li, "An improved k-nearest neighbor algorithm using tree structure and pruning technology," Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 35–48, 2019.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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