Outlier Detection Based on Local Kernel Regression for Instance Selection

doi:10.1080/18756891.2014.960230

Details of Research Outputs

Title	Outlier Detection Based on Local Kernel Regression for Instance Selection
Creator	Peng，Qinmu 1; Cheung，Yiu ming 1,2
Date Issued	2014-07-04
Source Publication	International Journal of Computational Intelligence Systems
ISSN	1875-6891
Volume	7 Issue:4 Pages:748-757
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.
Keyword	Instance Selection Local Kernel Regression Outlier Detection
DOI	10.1080/18756891.2014.960230
URL	View source
Language	英语English
Scopus ID	2-s2.0-84925936292
Citation statistics	Cited Times [WOS]:0 [WOS Record] [Related Records in WOS]
Document Type	Journal article
Identifier	http://repository.uic.edu.cn/handle/39GCC9TT/6510
Collection	Beijing Normal-Hong Kong Baptist University
Affiliation	1.Department of Computer Science,Hong Kong Baptist University,Hong Kong SAR,China 2.United International College,Beijing Normal University – Hong Kong Baptist University,Zhuhai,China
Recommended Citation GB/T 7714	Peng，Qinmu,Cheung，Yiu ming. Outlier Detection Based on Local Kernel Regression for Instance Selection[J]. International Journal of Computational Intelligence Systems, 2014, 7(4): 748-757.
APA	Peng，Qinmu, & Cheung，Yiu ming. (2014). Outlier Detection Based on Local Kernel Regression for Instance Selection. International Journal of Computational Intelligence Systems, 7(4), 748-757.
MLA	Peng，Qinmu,et al."Outlier Detection Based on Local Kernel Regression for Instance Selection". International Journal of Computational Intelligence Systems 7.4(2014): 748-757.

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