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dc.contributor.authorMaciąg, Piotr S.
dc.contributor.authorKryszkiewicz, Marzena
dc.contributor.authorBembenik, Robert
dc.contributor.authorL. Lobo, Jesus
dc.contributor.authorDel Ser, Javier
dc.date.accessioned2021-03-15T12:05:07Z
dc.date.available2021-03-15T12:05:07Z
dc.date.issued2021-07
dc.identifier.citationMaciąg, Piotr S., Marzena Kryszkiewicz, Robert Bembenik, Jesus L. Lobo, and Javier Del Ser. “Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks.” Neural Networks 139 (July 2021): 118–139. doi:10.1016/j.neunet.2021.02.017.en
dc.identifier.issn0893-6080en
dc.identifier.urihttp://hdl.handle.net/11556/1093
dc.description.abstractUnsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online evolving Spiking Neural Network for Unsupervised Anomaly Detection algorithm (OeSNN-UAD), which, unlike OeSNN, works in an unsupervised way and does not separate output neurons into disjoint decision classes. OeSNN-UAD uses our proposed new two-step anomaly detection method. Also, we derive new theoretical properties of neuronal model and input layer encoding of OeSNN, which enable more effective and efficient detection of anomalies in our OeSNN-UAD approach. The proposed OeSNN-UAD detector was experimentally compared with state-of-the-art unsupervised and semi-supervised detectors of anomalies in stream data from the Numenta Anomaly Benchmark and Yahoo Anomaly Datasets repositories. Our approach outperforms the other solutions provided in the literature in the case of data streams from the Numenta Anomaly Benchmark repository. Also, in the case of real data files of the Yahoo Anomaly Benchmark repository, OeSNN-UAD outperforms other selected algorithms, whereas in the case of Yahoo Anomaly Benchmark synthetic data files, it provides competitive results to the results recently reported in the literature.en
dc.description.sponsorshipP. Maciąg acknowledges financial Support of the Faculty of the Electronics and Information Technology of the Warsaw University of Technology, Poland (Grant No. II/2019/GD/1). J.L. Lobo and J. Del Ser would like to thank the Basque Government, Spain for their support through the ELKARTEK and EMAITEK funding programs. J. Del Ser also acknowledges funding support from the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education of the Basque Governmenten
dc.language.isoengen
dc.publisherElsevier Ltden
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleUnsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networksen
dc.typearticleen
dc.identifier.doi10.1016/j.neunet.2021.02.017en
dc.rights.accessRightsopenAccessen
dc.subject.keywordsEvolving Spiking Neural Networksen
dc.subject.keywordsOutliers detectionen
dc.subject.keywordsOnline learningen
dc.subject.keywordsTime series dataen
dc.subject.keywordsUnsupervised anomaly detectionen
dc.subject.keywordsStream dataen
dc.identifier.essn1879-2782en
dc.journal.titleNeural Networksen
dc.page.final139en
dc.page.initial118en
dc.volume.number139en


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