AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection Article Swipe
Marcin Pietroń
,
Dominik Żurek
,
Kamil Faber
,
Roberto Corizzo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.16497
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.16497
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and non-gradient fine tuning. However, existing methods mostly focus on optimizing model architectures without taking into account feature subspaces and model weights. In this work, we propose Anomaly Detection Neuroevolution (AD-NEv) - a scalable multi-level optimized neuroevolution framework for multivariate time series anomaly detection. The method represents a novel approach to synergically: i) optimize feature subspaces for an ensemble model based on the bagging technique; ii) optimize the model architecture of single anomaly detection models; iii) perform non-gradient fine-tuning of network weights. An extensive experimental evaluation on widely adopted multivariate anomaly detection benchmark datasets shows that the models extracted by AD-NEv outperform well-known deep learning architectures for anomaly detection. Moreover, results show that AD-NEv can perform the whole process efficiently, presenting high scalability when multiple GPUs are available.
Related Topics
Concepts
Anomaly detection
Computer science
Neuroevolution
Benchmark (surveying)
Scalability
Artificial intelligence
Interpretability
Anomaly (physics)
Multivariate statistics
Reconfigurability
Machine learning
Feature (linguistics)
Artificial neural network
Data mining
Pattern recognition (psychology)
Geography
Linguistics
Geodesy
Database
Telecommunications
Philosophy
Physics
Condensed matter physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.16497
- https://arxiv.org/pdf/2305.16497
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378713409
All OpenAlex metadata
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https://openalex.org/W4378713409Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.16497Digital Object Identifier
- Title
-
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-05-25Full publication date if available
- Authors
-
Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto CorizzoList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.16497Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.16497Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2305.16497Direct OA link when available
- Concepts
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Anomaly detection, Computer science, Neuroevolution, Benchmark (surveying), Scalability, Artificial intelligence, Interpretability, Anomaly (physics), Multivariate statistics, Reconfigurability, Machine learning, Feature (linguistics), Artificial neural network, Data mining, Pattern recognition (psychology), Geography, Linguistics, Geodesy, Database, Telecommunications, Philosophy, Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.AD-NEv | 165, 178 |
| abstract_inverted_index.method | 56, 110 |
| abstract_inverted_index.models | 162 |
| abstract_inverted_index.modern | 10 |
| abstract_inverted_index.mostly | 72 |
| abstract_inverted_index.neural | 60 |
| abstract_inverted_index.search | 55 |
| abstract_inverted_index.series | 106 |
| abstract_inverted_index.single | 136 |
| abstract_inverted_index.taking | 79 |
| abstract_inverted_index.widely | 152 |
| abstract_inverted_index.Anomaly | 0, 92 |
| abstract_inverted_index.Despite | 16 |
| abstract_inverted_index.account | 81 |
| abstract_inverted_index.adopted | 153 |
| abstract_inverted_index.anomaly | 25, 107, 137, 155, 172 |
| abstract_inverted_index.bagging | 128 |
| abstract_inverted_index.dataset | 32 |
| abstract_inverted_index.failure | 13 |
| abstract_inverted_index.feature | 82, 119 |
| abstract_inverted_index.methods | 4, 71 |
| abstract_inverted_index.models; | 139 |
| abstract_inverted_index.network | 145 |
| abstract_inverted_index.optimal | 59 |
| abstract_inverted_index.perform | 141, 180 |
| abstract_inverted_index.present | 5 |
| abstract_inverted_index.process | 183 |
| abstract_inverted_index.propose | 91 |
| abstract_inverted_index.results | 175 |
| abstract_inverted_index.tuning. | 68 |
| abstract_inverted_index.without | 78 |
| abstract_inverted_index.(AD-NEv) | 95 |
| abstract_inverted_index.However, | 69 |
| abstract_inverted_index.approach | 114 |
| abstract_inverted_index.datasets | 158 |
| abstract_inverted_index.ensemble | 123 |
| abstract_inverted_index.existing | 70 |
| abstract_inverted_index.gradient | 64 |
| abstract_inverted_index.learning | 22, 58, 169 |
| abstract_inverted_index.multiple | 189 |
| abstract_inverted_index.optimize | 118, 131 |
| abstract_inverted_index.problem, | 50 |
| abstract_inverted_index.process. | 39 |
| abstract_inverted_index.scalable | 98 |
| abstract_inverted_index.solution | 47 |
| abstract_inverted_index.systems. | 15 |
| abstract_inverted_index.weights. | 86, 146 |
| abstract_inverted_index.Detection | 93 |
| abstract_inverted_index.Moreover, | 174 |
| abstract_inverted_index.automated | 54 |
| abstract_inverted_index.benchmark | 157 |
| abstract_inverted_index.consuming | 38 |
| abstract_inverted_index.detection | 1, 138, 156 |
| abstract_inverted_index.effective | 44 |
| abstract_inverted_index.efficient | 46 |
| abstract_inverted_index.extensive | 148 |
| abstract_inverted_index.extracted | 163 |
| abstract_inverted_index.framework | 102 |
| abstract_inverted_index.networks, | 61 |
| abstract_inverted_index.optimized | 100 |
| abstract_inverted_index.subspaces | 83, 120 |
| abstract_inverted_index.available. | 192 |
| abstract_inverted_index.capability | 8 |
| abstract_inverted_index.cumbersome | 35 |
| abstract_inverted_index.detection, | 26 |
| abstract_inverted_index.detection. | 108, 173 |
| abstract_inverted_index.evaluation | 150 |
| abstract_inverted_index.fast-paced | 18 |
| abstract_inverted_index.optimizing | 75 |
| abstract_inverted_index.outperform | 166 |
| abstract_inverted_index.prediction | 14 |
| abstract_inverted_index.presenting | 185 |
| abstract_inverted_index.represents | 111 |
| abstract_inverted_index.supporting | 62 |
| abstract_inverted_index.technique; | 129 |
| abstract_inverted_index.well-known | 167 |
| abstract_inverted_index.development | 19 |
| abstract_inverted_index.fine-tuning | 143 |
| abstract_inverted_index.multi-level | 99 |
| abstract_inverted_index.scalability | 187 |
| abstract_inverted_index.architecture | 134 |
| abstract_inverted_index.efficiently, | 184 |
| abstract_inverted_index.experimental | 149 |
| abstract_inverted_index.multivariate | 104, 154 |
| abstract_inverted_index.non-gradient | 66, 142 |
| abstract_inverted_index.optimization | 28 |
| abstract_inverted_index.architectures | 23, 77, 170 |
| abstract_inverted_index.cyberphysical | 11 |
| abstract_inverted_index.synergically: | 116 |
| abstract_inverted_index.Neuroevolution | 40, 94 |
| abstract_inverted_index.neuroevolution | 101 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |