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In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a <jats:italic>context<\/jats:italic> of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.<\/jats:p>","DOI":"10.1007\/s10618-023-00967-z","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T17:01:45Z","timestamp":1691600505000},"page":"2517-2563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Explainable contextual anomaly detection using quantile regression forests"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1124-5778","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"first","affiliation":[]},{"given":"Matthijs","family":"van Leeuwen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"967_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-54765-7","volume-title":"Outlier ensembles: an introduction","author":"CC Aggarwal","year":"2017","unstructured":"Aggarwal CC, Sathe S (2017) Outlier ensembles: an introduction. 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