Improve the Detection of Clustered Outliers via Outlier Score Propagation

Li, Yongmou; Wang, Yijie; Guan, Hongtao

doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00155

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…But this did not work for outliers in high-density regions. Based on these problems, Li, Wang & Guan, 2019 proposed a graph-based outlier detection method that can significantly improve the performance of existing outlier detection methods. The method does not distinguish between local outliers and global outliers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since federated learning is an emerging field, its use in handling noise data is rarely covered. So this article refers to Ahmed et al (2020) , Ye et al (2020) , Li, Wang & Guan (2019) , Xu et al (2022) , Seth, Swain & Mishra (2018) , Zhang et al (2018) for a comparative analysis of federated learning algorithms applied to different domains with the mechanism proposed in this article, as shown in Table 1 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Still, it is but is not sufficient to better classify noise data and valid data. Therefore, in this article, based on the idea of Li, Wang & Guan (2019) , the performance of the outlier detection method can be improved by filtering out the good subset as the initial clustering centre of the outliers, and the algorithm expression of the method is shown below.…”

Section: Our Proposed Framework: Oes-fedmentioning

confidence: 99%

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OES-Fed: a federated learning framework in vehicular network based on noise data filtering

Lei

Wang

Caiyu

et al. 2022

PeerJ Computer Science

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The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.

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Section: Literature Reviewmentioning

confidence: 99%