A New Local Mean-based Nonparametric Classification Method

As an improved method of k-nearest neighbor classification, the local mean-based nonparametric classifier had the ability to resist the effects of noise and classify unbalanced data. When selecting the nearest k samples and calculating the distance between the test samples and the local mean-based vectors, it always used Euclidean distance. However, for multi-dimensional data, using Euclidean distance which focused on the difference of the value to determine whether two vectors was similar was not so accurate. To solve this problem, a new local mean-based nonparametric classification method was proposed in this paper. It used the cosine distance which focused more on the difference of the dimension to select the k nearest neighbors and compute the distance between the test samples and the local mean-based vectors. The new local mean-based nonparametric classification method was tested on the UCI datasets: Iris and Wine for different values of k in different test data set, the simulation results show that it outperforms the existing local mean-based nonparametric classifier.