DocumentCode :
3432391
Title :
Prediction-based outlier detection with explanations
Author :
Chen, Liang-Chieh ; Kuo, Tsung-Ting ; Lai, Wei-Chi ; Lin, Shou-De ; Tsai, Chi-Hung
Author_Institution :
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan
fYear :
2012
fDate :
11-13 Aug. 2012
Firstpage :
44
Lastpage :
49
Abstract :
General outlier detection strategies, be a distribution-based, clustering-based, or distance-based method, all resort to the comparison among instances to define abnormality. In this paper we introduce an additional dimension into the outlier definition. That is, we not only consider externally how one instance differs from others but internally the dependency and abnormality among its own attributes, denoted as the prediction-based outlier detection. Prediction-based outliers possess certain attributes which are difficult to be predicted based on the neighborhood information. Furthermore, we propose three neighborhood functions to generate predictions. Finally, acknowledging the lack of the gold standard to evaluate an outlier detection system, we propose four general evaluation strategies. Experiments conducted on several real-world datasets demonstrate the validity, novelty, power-law distribution, and robustness of our method.
Keywords :
Abstracts;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing (GrC), 2012 IEEE International Conference on
Conference_Location :
Hangzhou, China
Print_ISBN :
978-1-4673-2310-9
Type :
conf
DOI :
10.1109/GrC.2012.6468672
Filename :
6468672
Link To Document :
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