Department of Computer Science
Sample outlier detection based on local kernel regression
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.
instance selection, local kernel regression, outlier detection
Source Publication Title
Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops
Link to Publisher's Edition
Peng, Q., & Cheung, Y. (2012). Sample outlier detection based on local kernel regression. Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, 664-668. https://doi.org/10.1109/WI-IAT.2012.260