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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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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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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An Efficient Density-Based Local Outlier Detection Approach for Scattered Data
IEEE Access, 2019 After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore
Shubin Su +6 more
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An Incremental Local Outlier Detection Method in the Data Stream
Applied Sciences, 2018 Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging.
Haiqing Yao +3 more
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TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams
Sensors, 2020 Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT).
Jen-Wei Huang +2 more
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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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A Comparative Study for Outlier Detection Strategies Based On Traditional Machine Learning For IoT Data Analysis. [PDF]
IJCI International Journal of Computers and Information, 2022 Internets of Things (IoT) systems are increasing very fast. They have different types of wireless sensor networks (WSN) behind them. These networks have many applications that are a portion of our life such as healthcare, agricultural, mechanical, and ...
Khalid Amin +2 more
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Power Frequency Adaptive Suppression Method Based on Local Outlier Factor of Frequency Density [PDF]
Zhengzhou Daxue xuebao. Gongxue ban, 2023 Bioelectric signals belong to weak low-frequency signals with strong noise, therefore it is necessary to filter out power frequency interference. In order to ensure the accuracy and effectiveness of the filtering during power frequency offset, local ...
HUANG Zijuan, TU Juan, DAI Zunxiang
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