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Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Sample outlier detection based on local kernel regression

Language

English

Abstract

Outlier often degrades the classification and cluster accuracy. In this paper, we present 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 both on the synthetic and real-life data sets to show the efficacy of the proposed approach in comparison with the existing counterparts. © 2012 IEEE.

Keywords

instance selection, local kernel regression, outlier detection

Publication Date

2012

Source Publication Title

Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops

Start Page

664

End Page

668

Conference Location

Macau, China

Publisher

IEEE

ISBN (print)

9781467360579

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