A New Local Mean-based Nonparametric Classification Method

DOI

10.2991/icecee-15.2015.294How to use a DOI?

Keywords

k-nearest neighbor; local mean-based nonparametric classifier; new local mean-based nonparametric classification method

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoqin Zhang
AU  - Feng Liu
PY  - 2015/06
DA  - 2015/06
TI  - A New Local Mean-based Nonparametric Classification Method
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 1560
EP  - 1564
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.294
DO  - 10.2991/icecee-15.2015.294
ID  - Zhang2015/06
ER  -