Outlier Detection Based on Local Kernel Regression for Instance Selection

DOI

10.1080/18756891.2014.960230How to use a DOI?

Keywords

Outlier Detection, Instance Selection, Local Kernel Regression

Abstract

In this paper, we propose an outlier detection approach based on local kernel regression for instance selection. It evaluates the reconstruction error of instances by their neighbors to identify the outliers. Experiments are performed on the synthetic and real data sets to show the efficacy of the proposed approach in comparison with the existing counterparts.

Copyright

© 2017, 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  - JOUR
AU  - Qinmu Peng
AU  - Yiu-ming Cheung
PY  - 2014
DA  - 2014/08/01
TI  - Outlier Detection Based on Local Kernel Regression for Instance Selection
JO  - International Journal of Computational Intelligence Systems
SP  - 748
EP  - 757
VL  - 7
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.960230
DO  - 10.1080/18756891.2014.960230
ID  - Peng2014
ER  -