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Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection by Calikus, Ece; Nowaczyk, Slawomir; Bouguelia, Mohamed-Rafik; Dikmen, Onur is a scholarly article available to read on EtoBox.

What is Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection about?

In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets, with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we leverage active learning and ensembles to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. We propose a novel approach, called WisCon (Wisdom of the Contexts), that automatically creates contexts from the feature set. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual and non-contextual anomaly detectors, and supervised cla

Author

Calikus, Ece; Nowaczyk, Slawomir; Bouguelia, Mohamed-Rafik; Dikmen, Onur

Published

2021

Language

EN