Recently, many classification methods are widely used on real life data. K-nearest neighbor (KNN)
is one of the popular classification methods. Although KNN is a simple and popular classifier, it still has two
problems: including the classification accuracy is often worse than nonlinear classifiers such as support vec-
tor machine (SVM); the size of parameter k for KNN. To enhance the classification accuracy and to avoid the
sensitivity influence of parameter k, we propose a novel modified KNN method, the distance-based k-nearest
neighbor voting classifier (DBKNNV). In our study, the classification accuracy and the sensitivity of pa-
rameter k of DBKNNV are compared with KNN and two modified KNN methods. The experiment shows
that DBKNNV often achieves higher and more stable classification accuracy. Moreover, the influence with
the size of the parameter k of DBKNNV is not sensitivity. That means the classification accuracy of KNN
and two modified KNN methods are affected with the different parameter k setting. In contrast, the classifica-
tion accuracy of DBKNNV is more stable with different parameter k setting. Furthermore, the experiment al-
so shows the classification accuracies of DBKNNV and SVM are similar to each other.