Outlier Detection Based on Local Kernel Regression for Instance Selection

Qinmu Peng; Yiu Ming CHEUNG

doi:10.1080/18756891.2014.960230

Outlier Detection Based on Local Kernel Regression for Instance Selection

Qinmu Peng, Yiu Ming CHEUNG^*

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	748-757
Number of pages	10
Journal	International Journal of Computational Intelligence Systems
Volume	7
Issue number	4
DOIs	https://doi.org/10.1080/18756891.2014.960230
Publication status	Published - 4 Jul 2014

Scopus Subject Areas

Computer Science(all)
Computational Mathematics

User-Defined Keywords

Instance Selection
Local Kernel Regression
Outlier Detection

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10.1080/18756891.2014.960230

Cite this

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title = "Outlier Detection Based on Local Kernel Regression for Instance Selection",

abstract = "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.",

keywords = "Instance Selection, Local Kernel Regression, Outlier Detection",

author = "Qinmu Peng and CHEUNG, {Yiu Ming}",

note = "Funding Information: The works described in this paper were supported by the Faculty Research Grant of Hong Kong Baptist University with the Project Code: FRG2/11-12/067, FRG2/12-13/082, and the NSFC under grant 61272366.",

year = "2014",

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doi = "10.1080/18756891.2014.960230",

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T1 - Outlier Detection Based on Local Kernel Regression for Instance Selection

AU - Peng, Qinmu

AU - CHEUNG, Yiu Ming

N1 - Funding Information: The works described in this paper were supported by the Faculty Research Grant of Hong Kong Baptist University with the Project Code: FRG2/11-12/067, FRG2/12-13/082, and the NSFC under grant 61272366.

PY - 2014/7/4

Y1 - 2014/7/4

N2 - 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.

AB - 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.

KW - Instance Selection

KW - Local Kernel Regression

KW - Outlier Detection

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U2 - 10.1080/18756891.2014.960230

DO - 10.1080/18756891.2014.960230

M3 - Journal article

AN - SCOPUS:84925936292

SN - 1875-6891

VL - 7

SP - 748

EP - 757

JO - International Journal of Computational Intelligence Systems

JF - International Journal of Computational Intelligence Systems

IS - 4

ER -

Outlier Detection Based on Local Kernel Regression for Instance Selection

Abstract

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