| dc.description.abstract | This thesis studies the k-NN based classifier to overcome the negative effect of the
outliers. Sometimes data sets always exist values that are out of range and different from
the rest, these values are often called outliers. Outliers affect the training of the model and
give incorrect prediction results. This thesis aims to research and analyze the methods to
deal with this trouble and k-NN classifier was used to overcome the outliers in the data
sets. Methods developed based on the k-NN classifier including k-GNN, LMkNN and
LMkGNN were used to obtain the best results. Through experiments on five data sets
from UCI Repository, the results illustrated that the LMkGNN classifier gave the highest
accuracy of the four classifiers, and the ability to overcome the negative effect of outliers
of the LMkGNN was also higher than the three sets remaining | en_US |
