@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 (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.},
DOI = {10.31209/2018.100000003}
}