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Volume 37 Issue 12
Jan.  2016
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Ru Xiao-hu, Liu Zheng, Jiang Wen-li, Huang Zhi-tao. Normalized Residual-based Outlier Detection with False-alarm Probability Controlling[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2898-2905. doi: 10.11999/JEIT150469
Citation: Ru Xiao-hu, Liu Zheng, Jiang Wen-li, Huang Zhi-tao. Normalized Residual-based Outlier Detection with False-alarm Probability Controlling[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2898-2905. doi: 10.11999/JEIT150469

Normalized Residual-based Outlier Detection with False-alarm Probability Controlling

doi: 10.11999/JEIT150469
  • Received Date: 2015-04-22
  • Rev Recd Date: 2015-09-01
  • Publish Date: 2015-12-19
  • Outlier detection, also called anomaly detection, is an important issue in pattern recognition and knowledge discovery. Previous outlier detection methods can not effectively control the false-alarm probability. To solve the problem, a supervised method based on Normalized Residual (NR) is proposed. Using the training patterns, it first calculates the NR value of the query pattern, which is compared with a predefined detection threshold to determine whether the pattern is an outlier. In this paper, the relationship between the threshold and false-alarm probability is theoretically derived, based on which an appropriate threshold can be chosen. In this way, the desired false-alarm probability can be obtained even when few training patterns are available. Simulations and measured data experiments validate the superior performance of the proposed method on outlier detection and false-alarm probability controlling.
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