International Journal of Computational Intelligence Systems

Volume 7, Issue 4, August 2014, Pages 748 - 757

Outlier Detection Based on Local Kernel Regression for Instance Selection

Authors
Qinmu Peng, Yiu-ming Cheung
Corresponding Author
Qinmu Peng
Available Online 1 August 2014.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
7 - 4
Pages
748 - 757
Publication Date
2014/08
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2014.960230How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Qinmu Peng
AU  - Yiu-ming Cheung
PY  - 2014
DA  - 2014/08
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  - https://doi.org/10.1080/18756891.2014.960230
ID  - Peng2014
ER  -