Outlier Detection Based on Local Kernel Regression for Instance Selection

Qinmu Peng, Yiu Ming CHEUNG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)748-757
Number of pages10
JournalInternational Journal of Computational Intelligence Systems
Volume7
Issue number4
DOIs
Publication statusPublished - 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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