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Anomaly Detection in GPS Data Based on Visual Analytics

Anomaly Detection in GPS Data Based on Visual Analytics

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Anomaly Detection in GPS Data Based on Visual Analytics

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  1. Anomaly Detection in GPS Data Based on Visual Analytics Kyung Min Su - Zicheng Liao, Yizhou Yu, and Baoquan Chen, Anomaly Detection in GPS Data Based on Visual Analytics. IEEE Conference on Visual Analytics Science and Technology, 2010

  2. Overview • Data analysis on GPS traces of taxis • For traffic monitoring • To detect abnormal situations • Visual analytics approach • collaboration between machines and human analysts

  3. System architecture

  4. Feature Set

  5. Feature Extraction

  6. Probabilistic Models • Conditional Random Fields (CRF)

  7. Conditional Random Fields (CRF) • Hidden state sequence y • Z(x): normalization item

  8. CRF - Training • Training: computes the model parameters (theweight vector) according to labeled training data pairs {y, x}

  9. CRF - Inference • Inference: • tries to find the most likely hidden state assignment y, the label sequence for the unlabeled input sequence x

  10. Active Learning • Active learning: • learner selectivelychooses the examples • to reducedamount of training data • to improve the generalization performance on a fixed-size training set • Criteria • Uncertainty • Representativeness • Diversity

  11. Uncertainty • High model uncertainty • Help enrich the classifier • Confidence • Uncertainty

  12. Representativeness • High representativeness • sample sequenceis not similar to any other

  13. Diversity • Diversity: • To remove items that are redundant with respect to data items that are already in the training set from the previous iteration. • Similarity score is not greater than the average pairwise similarity among all sequences currently in the training set.

  14. Visualization and Interaction

  15. Interaction Interface • Basic mode • Raw GPS traces without any labels • Monitoring mode • Anomaly tags are shown. • Show the internal CRF states of the tagged data items. • Tagging mode • Active learning module is activated. • Highly uncertain labels fromthe CRF model are highlighted, requesting for user input.

  16. Visualizing CRF Features • CRF internal states visualization • Features and their Weights • Red: + • Negative: -

  17. Visualizing CRF Features

  18. Summarization • Anomaly detection system • Conditional Random Fields • Active Learning • Visualization and Interaction

  19. References • [1] Zicheng Liao, Yizhou Yu, and Baoquan Chen. Anomaly Detection in GPS Data Based on Visual Analytics. IEEE Conference on Visual Analytics Science and Technology (VAST 2010), 2010. • [2] J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning (ICML-2001), 2001. • [3] C. T. Symons, N. F. Samatova, R. Krishnamurthy, B. H. Park, T. Umar, D. Buttler, T. Critchlow, and D. Hysom. Multi-criterion active learning in conditional random fields. In Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, 2006.