@Article{2018.100000003,
AUTHOR = {Juan Li},
TITLE = {An Improved K-nearest Neighbor Algorithm Using Tree Structure and Pruning Technology},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {1},
PAGES = {35--48},
URL = {http://www.techscience.com/iasc/v25n1/39631},
ISSN = {2326-005X},
ABSTRACT = {K-Nearest Neighbor algorithm (<i>KNN</i>) is a simple and mature classification 
method. However there are susceptible factors influencing the classification 
performance, such as <i>k</i> 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 <i>k</i> value method was proposed. The proposed algorithm can 
overcome the shortcoming of <i>KNN</i>, 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 <i>KNN</i> 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.},
DOI = {10.31209/2018.100000003}
}