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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28240
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dc.contributor.authorSouto Arias, Luis Antonio-
dc.contributor.authorOosterlee, Cornelis W.-
dc.contributor.authorCirillo, Pasquale-
dc.date.accessioned2023-07-11T17:27:49Z-
dc.date.available2023-07-11T17:27:49Z-
dc.date.issued2023-
dc.identifier.issn0031-3203de_CH
dc.identifier.issn1873-5142de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28240-
dc.description.abstractMany unsupervised anomaly detection algorithms rely on the concept of nearest neighbours to compute the anomaly scores. Such algorithms are popular because there are no assumptions about the data, making them a robust choice for unstructured datasets. However, the number (k) of nearest neighbours, which critically affects the model performance, cannot be tuned in an unsupervised setting. Hence, we propose the new and parameter-free Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm, that combines the metrics of distance with isolation. Based on AIDA, we also introduce the Tempered Isolation-based eXplanation (TIX) algorithm, which identifies the most relevant features characterizing an outlier, even in large multi-dimensional datasets, improving the overall explainability of the detection mechanism. Both AIDA and TIX are thoroughly tested and compared with state-of-the-art alternatives, proving to be useful additions to the existing set of tools in anomaly detection.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofPattern Recognitionde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAnomaly explanationde_CH
dc.subjectDistancede_CH
dc.subjectEnsemble methodde_CH
dc.subjectIsolationde_CH
dc.subjectOutlier detectionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleAIDA : analytic isolation and distance-based anomaly detection algorithmde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitInstitut für Wirtschaftsinformatik (IWI)de_CH
dc.identifier.doi10.1016/j.patcog.2023.109607de_CH
dc.identifier.doi10.21256/zhaw-28240-
zhaw.funding.euNode_CH
zhaw.issue109607de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume141de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Souto Arias, L. A., Oosterlee, C. W., & Cirillo, P. (2023). AIDA : analytic isolation and distance-based anomaly detection algorithm. Pattern Recognition, 141(109607). https://doi.org/10.1016/j.patcog.2023.109607
Souto Arias, L.A., Oosterlee, C.W. and Cirillo, P. (2023) ‘AIDA : analytic isolation and distance-based anomaly detection algorithm’, Pattern Recognition, 141(109607). Available at: https://doi.org/10.1016/j.patcog.2023.109607.
L. A. Souto Arias, C. W. Oosterlee, and P. Cirillo, “AIDA : analytic isolation and distance-based anomaly detection algorithm,” Pattern Recognition, vol. 141, no. 109607, 2023, doi: 10.1016/j.patcog.2023.109607.
SOUTO ARIAS, Luis Antonio, Cornelis W. OOSTERLEE und Pasquale CIRILLO, 2023. AIDA : analytic isolation and distance-based anomaly detection algorithm. Pattern Recognition. 2023. Bd. 141, Nr. 109607. DOI 10.1016/j.patcog.2023.109607
Souto Arias, Luis Antonio, Cornelis W. Oosterlee, and Pasquale Cirillo. 2023. “AIDA : Analytic Isolation and Distance-Based Anomaly Detection Algorithm.” Pattern Recognition 141 (109607). https://doi.org/10.1016/j.patcog.2023.109607.
Souto Arias, Luis Antonio, et al. “AIDA : Analytic Isolation and Distance-Based Anomaly Detection Algorithm.” Pattern Recognition, vol. 141, no. 109607, 2023, https://doi.org/10.1016/j.patcog.2023.109607.


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