Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories
Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Gen...
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Published in | Annals of mathematics and artificial intelligence Vol. 74; no. 1-2; pp. 67 - 94 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
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Springer International Publishing
01.06.2015
Springer Nature B.V |
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Abstract | Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Generally, previous anomaly detection algorithms often require tuning of many parameters, including ad-hoc anomaly thresholds, which may result in overfitting and high alarm rates. The main contributions of this paper are two-fold: The first is the proposal and analysis of the
Inductive Conformal Anomaly Detector
(ICAD), which is a general and parameter-light anomaly detection algorithm that has well-calibrated alarm rate. ICAD is a generalisation of the previously proposed
Conformal Anomaly Detector
(CAD) based on the concept of
Inductive Conformal Predictors
. The main advantage of ICAD compared to CAD is the improved computational efficiency. The only design parameter of ICAD is the
Non-Conformity Measure
(NCM). The second contribution of this paper concerns the proposal and investigation of the
Sub-Sequence Local Outlier
(SSLO) NCM, which is designed for sequential detection of anomalous sub-trajectories in the framework of ICAD. SSLO-NCM is based on
Local Outlier Factor
(LOF) and is therefore sensitive to local sub-trajectory anomalies. The results from the empirical investigations on an unlabelled set of vessel trajectories illustrate the most anomalous trajectories detected for different parameter values of SSLO-NCM, and confirm that the empirical alarm rate is indeed well-calibrated. |
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AbstractList | Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Generally, previous anomaly detection algorithms often require tuning of many parameters, including ad-hoc anomaly thresholds, which may result in overfitting and high alarm rates. The main contributions of this paper are two-fold: The first is the proposal and analysis of the Inductive Conformal Anomaly Detector (ICAD), which is a general and parameter-light anomaly detection algorithm that has well-calibrated alarm rate. ICAD is a generalisation of the previously proposed Conformal Anomaly Detector (CAD) based on the concept of Inductive Conformal Predictors. The main advantage of ICAD compared to CAD is the improved computational efficiency. The only design parameter of ICAD is the Non-Conformity Measure (NCM). The second contribution of this paper concerns the proposal and investigation of the Sub-Sequence Local Outlier (SSLO) NCM, which is designed for sequential detection of anomalous sub-trajectories in the framework of ICAD. SSLO-NCM is based on Local Outlier Factor (LOF) and is therefore sensitive to local sub-trajectory anomalies. The results from the empirical investigations on an unlabelled set of vessel trajectories illustrate the most anomalous trajectories detected for different parameter values of SSLO-NCM, and confirm that the empirical alarm rate is indeed well-calibrated. Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Generally, previous anomaly detection algorithms often require tuning of many parameters, including ad-hoc anomaly thresholds, which may result in overfitting and high alarm rates. The main contributions of this paper are two-fold: The first is the proposal and analysis of the Inductive Conformal Anomaly Detector (ICAD), which is a general and parameter-light anomaly detection algorithm that has well-calibrated alarm rate. ICAD is a generalisation of the previously proposed Conformal Anomaly Detector (CAD) based on the concept of Inductive Conformal Predictors . The main advantage of ICAD compared to CAD is the improved computational efficiency. The only design parameter of ICAD is the Non-Conformity Measure (NCM). The second contribution of this paper concerns the proposal and investigation of the Sub-Sequence Local Outlier (SSLO) NCM, which is designed for sequential detection of anomalous sub-trajectories in the framework of ICAD. SSLO-NCM is based on Local Outlier Factor (LOF) and is therefore sensitive to local sub-trajectory anomalies. The results from the empirical investigations on an unlabelled set of vessel trajectories illustrate the most anomalous trajectories detected for different parameter values of SSLO-NCM, and confirm that the empirical alarm rate is indeed well-calibrated. |
Author | Falkman, Göran Laxhammar, Rikard |
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Cites_doi | 10.1007/978-94-015-3994-4 10.1109/TCSVT.2008.2005599 10.1007/s10115-008-0131-9 10.1093/comjnl/bxl065 10.1016/j.sigpro.2003.07.018 10.1145/357830.357849 10.1007/s00530-006-0058-5 10.1145/1541880.1541882 10.1109/TITS.2010.2048101 10.1007/s10618-006-0049-3 10.1109/TCSVT.2008.927109 10.5244/C.22.103 10.1117/12.800095 10.1109/ICDE.2008.4497422 10.1109/ITAB.2009.5394447 10.1007/978-3-540-73499-4_6 10.5772/6078 10.1109/ICTAI.2007.47 10.1007/978-3-642-16239-8_8 10.1145/342009.335388 10.1109/ICDM.2005.79 |
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Keywords | 68T10 68T05 Maritime surveillance Trajectory data Local outlier factor Anomaly detection Conformal prediction |
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SubjectTerms | Algorithms Anomalies Anomaly detection Artificial Intelligence Complex Systems Computer Science Conformal prediction Data analysis Design parameters Empirical analysis Local outlier factor Machine learning Maritime surveillance Mathematics Outliers (statistics) Skövde Artificial Intelligence Lab (SAIL) Technology Teknik Trajectory data |
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Title | Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories |
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