Fluctuation-based outlier detection [PDF]
Scientific Reports, 2023 Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects.
Xusheng Du +4 more
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Outlier detection using iterative adaptive mini-minimum spanning tree generation with applications on medical data [PDF]
Frontiers in Physiology, 2023 As an important technique for data pre-processing, outlier detection plays a crucial role in various real applications and has gained substantial attention, especially in medical fields.
Jia Li +7 more
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A novel subspace outlier detection method by entropy-based clustering algorithm [PDF]
Scientific Reports, 2023 Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the “curse of dimensionality”. Subspace outlier detection methods have
Zheng Zuo +3 more
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FRIOD: A Deeply Integrated Feature-Rich Interactive System for Effective and Efficient Outlier Detection [PDF]
IEEE Access, 2017 In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the ...
Xiaodong Zhu +5 more
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A Survey of Outlier Detection Methodologies [PDF]
Artificial Intelligence Review, 2004 Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply ...
Victoria J Hodge, Jim Austin
exaly +7 more sources
Outlier Detection Techniques for Wireless Sensor Networks: A Survey [PDF]
IEEE Communications Surveys and Tutorials, 2010 In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers.
Nirvana Meratnia, Paul Havinga
exaly +6 more sources
Outlier Detection in Mendelian Randomization. [PDF]
Stat MedABSTRACT Mendelian randomization (MR) uses genetic variants as instrumental variables to infer causal effects of exposures on an outcome. One key assumption of MR is that the genetic variants used as instrumental variables are independent of the outcome conditional on the risk factor and unobserved confounders.
Mandl MM +3 more
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An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data [PDF]
Entropy, 2023 Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data ...
Zihao Li, Liumei Zhang
doaj +2 more sources
Locality and Consistency Based Sequential Ensemble Method for Outlier Detection [PDF]
Jisuanji kexue, 2022 Outlier detection has been widely used in many fields,such as network intrusion detection,credit card fraud detection,etc.The increase in data dimensions leads to many irrelevant and redundant features,which will obscure the relevant features and result ...
LIU Yi, MAO Ying-chi, CHENG Yang-kun, GAO Jian, WANG Long-bao
doaj +1 more source
Online Bivariate Outlier Detection in Final Test Using Kernel Density Estimation [PDF]
, 2011 In parametric IC testing, outlier detection is applied to filter out potential unreliable devices. Most outlier detection methods are used in an offline setting and hence are not applicable to Final Test, where immediate pass/fail decisions are required.
Bossers, H.C.M. +2 more
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