About: LoPAD: A Local Prediction Approach to Anomaly Detection

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	LoPAD: A Local Prediction Approach to Anomaly Detection
Creator	Liu, Lin Le, Duy Li, Jiuyong Liu, Jixue Lu, Sha
Source	PMC
abstract	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.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_50
has license	no-cc
sha1sum (hex)	2e4725949ac016efc6a566edb088a3746109a117
schema:url	https://doi.org/10.1007/978-3-030-47436-2_50
resource representing a document's title	LoPAD: A Local Prediction Approach to Anomaly Detection
has PubMed Central identifier	PMC7206231
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:2e4725949ac016efc6a566edb088a3746109a117#body_text
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