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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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  • Hawkins D. Identification of Outliers[M]. London: Chapman and Hall, 1980: Chapter 1-2.
    Liu J, Wan J, Zheng H, et al.. A method of specific emitter verification based on CSDA and SVDD[C]. Proceedings of the IEEE 2nd International Conference on Computer Science and Network Technology, Changchun, China, 2012: 562-565.
    Miller D J and Browning J. A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled/unlabeled data sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(11): 1468-1483.
    Westerweel J and Scarano F. Universal outlier detection for PIV data[J]. Experiments in Fluids, 2005, 39(6): 1096-1100.
    Duncan J, Dabiri D, Hove J, et al.. Universal outlier detection for Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) data[J]. Measurement Science and Technology, 2010, 21(5): 57002-57006.
    Wu S and Wang S R. Information-theoretic outlier detection for large-scale categorical data[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 589-602.
    Li Z G, Baseman R J, Zhu Y D, et al.. A unified framework for outlier detection in trace data analysis[J]. IEEE Transactions on Semiconductor Manufacturing, 2014, 27(1): 95-103.
    Albanese A, Pal S K, and Petrosino A. Rough sets, kernel set, and spatiotemporal outlier detection[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 194-207.
    Ghosh A K and Chaudhuri P. On maximum depth and related classifiers[J]. Scandinavian Journal of Statistics, 2005, 32(2): 327-350.
    Ru X H, Liu Z, and Jiang W L. Normalized residual-based outlier detection[C]. Proceedings of the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Guilin, China, 2014: 190-193.
    Nattorn B, Arthorn L, and Krung S. Outlier detection score based on ordered distance difference[C]. Proceedings of the IEEE International Computer Science and Engineering Conference (ICSEC), Nakhon Pathom, Thailand, 2013: 157-162.
    Zhao M and Saligrama V. Anomaly detection with score functions based on nearest neighbor graphs[J]. Advances in Neural Information Processing Systems, 2009, 22(1): 2250-2258.
    Qian J and Saligrama V. New statistic in p-value estimation for anomaly detection[C]. Proceedings of the IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Michigan, USA, 2012: 393-396.
    Chen Y T, Qian J, and Saligrama V. A new one-class SVM for anomaly detection[C]. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 2013: 3567-3571.
    Breunig M M, Kriegel H-P, Ng R T, et al.. LOF: identifying density-based local outliers[C]. Proceedings of the ACM SIGMOD International Conference on Management of Data, New York, USA, 2000: 93-104.
    Sch?lkopf B, Platt J C, Shawe-Taylor J C, et al.. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7): 1443-1471.
    Furlani M, Tuia D, Munoz-Mari J, et al.. Discovering single classes in remote sensing images with active learning[C]. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 2012: 7341-7344.
    Jumutc V and Suykens J. Multi-class supervised novelty detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(12): 2510-2523.
    叶浩欢, 柳征, 姜文利. 考虑多普勒效应的脉冲无意调制特征比较[J]. 电子与信息学报, 2012, 34(11): 2654-2659.
    Ye H H, Liu Z, and Jiang W L. A comparison of unintentional modulation on pulse features with the consideration of Doppler effect[J]. Journal of Electronics Information Technology, 2012, 34(11): 2654-2659.
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