Juan Li
Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003
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… More >
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