Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF) [PDF]
Sensors, 2022 Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step.
Velu Prabhakar Kumaravel +3 more
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Outlier detection algorithm based on k-nearest neighbors-local outlier factor
Journal of Algorithms & Computational Technology, 2022 The main task of outlier detection is to detect data objects which have a different mechanism from the conventional data set. The existing outlier detection methods are mainly divided into two directions: local outliers and global outliers. Aiming at the
He Xu, Lin Zhang, Peng Li, Feng Zhu
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A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams [PDF]
Big Data and Cognitive Computing, 2020 Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning.
Omar Alghushairy +3 more
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Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm [PDF]
Diagnostics, 2022 Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but
Tingting He, Qiaoer Zhou, Yuanwen Zou
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Outlier Detection and Explanation Method Based on FOLOF Algorithm [PDF]
EntropyOutlier mining constitutes an essential aspect of modern data analytics, focusing on the identification and interpretation of anomalous observations. Conventional density-based local outlier detection methodologies frequently exhibit limitations due to ...
Lei Bai, Jiasheng Wang, Yu Zhou
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Using local outlier factor to detect fraudulent claims in auto insurance [PDF]
Mathematics and Modeling in Finance, 2022 Given the significant increase in fraudulent claims and the resulting financial losses, it is important to adopt a scientific approach to detect and prevent such cases.
Maryam Esna-Ashari +2 more
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Clustering-Based Outlier Detection Technique Using PSO-KNN
Journal of Applied Science and Engineering, 2023 In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique.
Sushilata D. Mayanglambam +2 more
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Clarifying the Hubble constant tension with a Bayesian hierarchical model of the local distance ladder [PDF]
, 2017 Estimates of the Hubble constant, $H_0$, from the distance ladder and the cosmic microwave background (CMB) differ at the $\sim$3-$\sigma$ level, indicating a potential issue with the standard $\Lambda$CDM cosmology.
Dalmasso, Niccolò +2 more
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An Outlier Detection Approach Based on Improved Self-Organizing Feature Map Clustering Algorithm
IEEE Access, 2019 Local Outlier Factor (LOF) outlier detecting algorithm has good accuracy in detecting global and local outliers. However, the algorithm needs to traverse the entire dataset when calculating the local outlier factor of each data point, which adds extra ...
Ping Yang +4 more
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Dynamic graph embedding for outlier detection on multiple meteorological time series.
PLoS ONE, 2021 Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. To overcome this limitation for the effective detection of abnormal climatic events from meteorological time ...
Gen Li, Jason J Jung
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