Unbiased Simulation of Rare Events in Continuous Time

Hodgson, James; Johansen, Adam M.; Pollock, Murray

doi:10.1007/s11009-021-09886-2

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…However, binary classification tasks can be used in time series data to discriminate between events with the goal to detect anomalies in the data. A method that combines strong simulation and multilevel splitting to estimate rare event probabilities in Markov processes was proposed in [26] with strong simulation ideas to avoid bias but there is a need for improvement in the scalability of the method. The utilization of the XGBoost and AdaBoost models has been employed in [57] for training the predictive models.…”

Section: Classificationmentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

Abubakar,

Othmani,

Siarry

et al. 2024

IEEE Access

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Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.

show abstract

Section: Classificationmentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

Abubakar,

Othmani,

Siarry

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, binary classification tasks can be used in time series data to discriminate between events with the goal to detect anomalies in the data. A method that combines strong simulation and multilevel splitting to estimate rare event probabilities in Markov processes was proposed in [88] with strong simulation ideas to avoid bias but there is a need for improvement in the scalability of the method. The utilization of the XGBoost and AdaBoost models has been employed in [85] for training the predictive models.…”

Section: Classificationmentioning

confidence: 99%

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

Abubakar,

OTHMANI,

Sabri

et al. 2023

Preprint

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<p>Rare event detection (RED) involves the identification and detection of events characterized by low frequency of occurrences, but of high importance or impact. This paper presents a Systematic Review (SR) of rare event detection across various modalities using Machine Learning (ML) and Deep Learning (DL) techniques. This review comprehensively outlines techniques and methods best suited for rare event detection across various modalities, while also highlighting future research prospects. To the extent of our knowledge, this paper is a pioneering SR dedicated to exploring this specific research domain. This SR identifies the employed methods and techniques, the datasets utilized, and the effectiveness of these methods in detecting rare events. Four modalities concerning RED are reviewed in this SR: video, sound, image, and time series. The corresponding performances for the different ML and DL techniques for RED are discussed comprehensively, together with the associated RED challenges and limitations as well as the directions for future research are highlighted. This SR aims to offer a comprehensive overview of the existing methods in RED, serving as a valuable resource for researchers and practitioners working in the respective field.</p>

show abstract

A Systematic Review of Rare Events Detection Using Machine Learning and Deep Learning

Idris Abubakar,

OTHMANI,

Siarry

et al. 2023

Preprint

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Unbiased Simulation of Rare Events in Continuous Time

Cited by 3 publications

References 31 publications

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning

A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning

A Systematic Review of Rare Events Detection Using Machine Learning and Deep Learning

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