A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data Streams Article Swipe
Théo Delemazure
,
Chris Dong
,
Dominik Peters
,
Magdalena Tydrichova
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2024/305
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2024/305
Online Anomaly Detection (OAD) is critical for identifying rare yet important data points in large, dynamic, and complex data streams. A key challenge lies in achieving accurate and consistent detection of anomalies while maintaining computational and memory efficiency. Conventional OAD approaches, which depend on distributional deviations and static thresholds, struggle with model update delays and catastrophic forgetting, leading to missed detections and high false positive rates. To address these limitations, we propose a novel Streaming Anomaly Detection (SAD) method, grounded in a sparse active online learning framework. Our approach uniquely integrates ℓ1,2-norm sparse online learning with CUR decomposition-based active learning, enabling simultaneous fast feature selection and dynamic instance selection. The efficient CUR decomposition further supports real-time residual analysis for anomaly scoring, eliminating the need for manual threshold settings about temporal data distributions. Extensive experiments on diverse streaming datasets demonstrate SAD's superiority, achieving a 14.06% reduction in detection error rates compared to five state-of-the-art competitors.
Related Topics
Concepts
Social choice theory
Computer science
Mathematical economics
Ballot
Voting
Class (philosophy)
Set (abstract data type)
Axiom
Interval (graph theory)
Ideal (ethics)
Impossibility
Selection (genetic algorithm)
Order (exchange)
Monotonic function
Mathematics
Politics
Artificial intelligence
Law
Political science
Economics
Combinatorics
Mathematical analysis
Geometry
Finance
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2024/305
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401024394
All OpenAlex metadata
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https://openalex.org/W4401024394Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2024/305Digital Object Identifier
- Title
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A Novel Sparse Active Online Learning Framework for Fast and Accurate Streaming Anomaly Detection Over Data StreamsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-26Full publication date if available
- Authors
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Théo Delemazure, Chris Dong, Dominik Peters, Magdalena TydrichovaList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2024/305Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://hal.science/hal-04727485Direct OA link when available
- Concepts
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Social choice theory, Computer science, Mathematical economics, Ballot, Voting, Class (philosophy), Set (abstract data type), Axiom, Interval (graph theory), Ideal (ethics), Impossibility, Selection (genetic algorithm), Order (exchange), Monotonic function, Mathematics, Politics, Artificial intelligence, Law, Political science, Economics, Combinatorics, Mathematical analysis, Geometry, Finance, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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