SPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection

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dc.contributor.authorNaser, M. Z.
dc.contributor.authorNaser, Ahmad Z.
dc.date.accessioned2025-05-02T19:23:05Z
dc.date.available2025-05-02T19:23:05Z
dc.date.issued2025-04-01
dc.date.updated2025-05-01T03:37:58Z
dc.description.abstractAbstract This paper presents a novel anomaly and outlier detection algorithm from the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This algorithm leverages the concept of similarity and higher-order interactions across multiple subspaces to identify outliers. A comprehensive set of experiments was conducted to evaluate the performance of SPINEX. This algorithm was examined against 21 commonly used anomaly detection algorithms, and across 39 synthetic and real datasets from various domains and of a variety of dimensions and complexities. Furthermore, a complexity analysis was carried out to examine the complexity of the proposed algorithm. Our results demonstrate that SPINEX achieves superior performance, outperforms commonly used anomaly detection algorithms, and has moderate complexity (e.g., O(n log n × d)). More specifically, SPINEX was found to rank at the top of algorithms on the synthetic datasets and the 7th on the real datasets. Finally, a demonstration of the explainability capabilities of SPINEX, along with future research needs, is presented.
dc.identifier.citationJournal of Big Data. 2025 Apr 01;12(1):77
dc.identifier.doi10.1186/s40537-025-01122-9
dc.identifier.urihttp://hdl.handle.net/1993/39065
dc.language.isoeng
dc.language.rfc3066en
dc.publisherSpringerOpen
dc.rights.holderThe Author(s)
dc.subjectAlgorithm
dc.subjectMachine learning
dc.subjectBenchmarking
dc.subjectAnomaly detection
dc.titleSPINEX-anomaly: similarity-based predictions with explainable neighbors exploration for anomaly and outlier detection
dc.typeresearch article
local.author.affiliationPrice Faculty of Engineering::Department of Mechanical Engineering
oaire.citation.startPage77
oaire.citation.titleJournal of Big Data
oaire.citation.volume12
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