Anomaly Detection in GPS Data Based on Visual AnalyticsKyung 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

Overview

Data analysis on GPS traces of taxis For traffic monitoring To detect abnormal situations

Visual analytics approach collaboration between machines and human

analysts

System architecture

Feature Set

Feature Extraction

Probabilistic Models

Conditional Random Fields (CRF)

Conditional Random Fields (CRF) Hidden state sequence y

Z(x): normalization item

CRF - Training

Training: computes the model parameters (the weight vector) according to labeled training data pairs {y, x}

CRF - Inference

Inference: tries to find the most likely hidden state

assignment y, the label sequence for the unlabeled input sequence x

Active Learning

Active learning: learner selectively chooses the examples to reduced amount of training data to improve the generalization performance on a

fixed-size training set

Criteria Uncertainty Representativeness Diversity

Uncertainty

High model uncertainty Help enrich the classifier

Confidence

Uncertainty

Representativeness

High representativeness sample sequence is not similar to any other

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.

Visualization and Interaction

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 from the CRF model are

highlighted, requesting for user input.

Visualizing CRF Features

CRF internal states visualization

Features and their Weights Red: + Negative: -

Visualizing CRF Features

Summarization

Anomaly detection system Conditional Random Fields

Active Learning

Visualization and Interaction

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.