An outlier detection algorithm based on an integrated outlier factor

Zhou, Huanfu; Liu, Hongjiang; Zhang, Yingjie; Zhang, Yao

doi:10.3233/ida-184227

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Of course, mechanisms to prevent cheating are indispensable in the development of blockchain digital currencies. In terms of outlier detection, Zhou, H. proposed an improved LOF outlier detection algorithm [14], which has significantly improved detection accuracy and can effectively detect abnormal data.…”

Section: Objectives Thought Process and Approachmentioning

confidence: 99%

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Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency

Qian

Zhang

et al. 2023

Mathematics

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As global inflation escalates and geopolitical conflicts exacerbate, the world’s economy confronts an intensified degree of instability. In this volatile environment, blockchain currencies emerge as a potential bulwark, offering both value preservation and liquidity benefits. However, the conventional “mining” process introduces significant challenges, such as high energy consumption, data security risks, and detachment from the real economy, which potentially facilitate financial capital manipulation. This research endeavors to mitigate these issues, constructing an innovative blockchain cryptocurrency framework that integrates mining and distribution with intelligent big data. It also incorporates social contributions from individuals in domains such as health, knowledge, and ecological conservation. Consequently, the efficiency of cryptocurrency production and distribution correlates with the individual’s societal contribution. The more substantial the contribution, the higher the intrinsic value of the individual and the more efficient the access. Utilizing a comprehensive framework of mathematical modeling, computer numerical simulation, and fuzzy integrated evaluation, we propose a novel endogenous-value blockchain cryptocurrency system. We quantify and optimize variables such as individual intrinsic value, community efficiency, redistribution weights, and total monetary potential. We introduce an innovative method for accumulating time-decaying values such as knowledge contribution and establish an anti-cheating framework. Our results indicate that this pioneering approach can significantly enhance mining efficiency and optimize cryptocurrency distribution. This counters traditional criticisms of blockchain currencies and paves the way for a more sustainable, fair, and efficient model for future blockchain currency systems.

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Section: Objectives Thought Process and Approachmentioning

confidence: 99%

“…It can be seen that the portion of memory retention decays gradually with time, but the trend of decay is faster and then slower. Therefore, this paper can achieve a similar forgetting effect by fitting an exponential function with the parameters shown in Formula (14).…”

Section: The Ebbinghaus Forgetting Curvementioning

confidence: 99%

Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency

Qian

Zhang

et al. 2023

Mathematics

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“…We select health-related dataset with the size of 5000, 10000, 20000, and 50000 randomly, and experiments are performed in selected dataset in order to demonstrate that the proposed outlier detection algorithm is more efficient in running time. We choose relative kernel density-based outlier score (RKDOS) (Wahid & Rao, 2020), influenced outlierness (INFLO) (Zhou et al, 2019), and local distance-based outlier detection factor (LDOF) (Radovanović et al, 2015) for comparison.…”

Section: Data Sourcementioning

confidence: 99%

“…In (Shao et al, 2021), an advanced fast density peak outlier detection algorithm based on the characteristics of big data was proposed to avoid the clustering process and reduce the running time of the cluster-based outlier detection algorithm. In (Zhou et al, 2019), a novel outlier detection as was proposed to integrate the local density with the global distance seamlessly. In the proposed method, an integrated outlier factor was used to measure the detecting accuracy.…”

Section: Related Workmentioning

confidence: 99%

AI for Health-Related Data Modeling

Cheng¹

2022

International Journal of Information System Modeling and Design

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Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.

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Open Circuit Fault Diagnosis of Modular Multilevel Converter Based on Local Outlier Factor Detection

Si,

Feng,

et al. 2024

2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)

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An outlier detection algorithm based on an integrated outlier factor

Cited by 4 publications

References 17 publications

Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency

Advances on External Machine Computing Power Focusing on Internal Personal Value: A Case Study on the New Digital Currency

AI for Health-Related Data Modeling

Open Circuit Fault Diagnosis of Modular Multilevel Converter Based on Local Outlier Factor Detection

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