Metadata about: Dependency-based anomaly detection methods detect anomalies by looking at the deviations from the normal probabilistic dependency among variables and are able to discover more subtle and meaningful anomalies. However, with high dimensional data, they face two key challenges. One is how to find the right set of relevant variables for a given variable from the large search space to assess dependency deviation. The other is how to use the dependency to estimate the expected value of a variable accurately. In this paper, we propose the Local Prediction approach to Anomaly Detection (LoPAD) framework to deal with the two challenges simultaneously. Through introducing Markov Blanket into dependency-based anomaly detection, LoPAD decomposes the high dimensional unsupervised anomaly detection problem into local feature selection and prediction problems while achieving better performance and interpretability. The framework enables instantiations with off-the-shelf predictive models for anomaly detection. Comprehensive experiments have been done on both synthetic and real-world data. The results show that LoPAD outperforms state-of-the-art anomaly detection methods.

About: Dependency-based anomaly detection methods detect anomalies by looking at the deviations from the normal probabilistic dependency among variables and are able to discover more subtle and meaningful anomalies. However, with high dimensional data, they face two key challenges. One is how to find the right set of relevant variables for a given variable from the large search space to assess dependency deviation. The other is how to use the dependency to estimate the expected value of a variable accurately. In this paper, we propose the Local Prediction approach to Anomaly Detection (LoPAD) framework to deal with the two challenges simultaneously. Through introducing Markov Blanket into dependency-based anomaly detection, LoPAD decomposes the high dimensional unsupervised anomaly detection problem into local feature selection and prediction problems while achieving better performance and interpretability. The framework enables instantiations with off-the-shelf predictive models for anomaly detection. Comprehensive experiments have been done on both synthetic and real-world data. The results show that LoPAD outperforms state-of-the-art anomaly detection methods.

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