Results 11 to 20 of about 89,907 (213)
Detecting Outliers in Non-IID Data: A Systematic Literature Review
IEEE Access, 2023 Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and ...
Shafaq Siddiqi +3 more
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Review of Outlier Detection Algorithms [PDF]
Jisuanji kexueOutlier detection,as an important research direction in the field of data mining,aims to discover data points in a dataset that are different from the majority and have potential analytical value,assistresearchers in identifying potential issues in the ...
KONG Lingchao, LIU Guozhu
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A Comparison of Outlier Detection Techniques for High-Dimensional Data
International Journal of Computational Intelligence Systems, 2018 Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly.
Xiaodan Xu +3 more
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A Novel Outlier Detection Model for Vibration Signals Using Transformer Networks
IEEE Access, 2022 Outlier detection in vibration signals can play an important role in addressing the issue of structural or environmental changes during vibration testing. In this study, a transformer-based model for outlier detection is proposed.
Ruiheng Zhang +4 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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A Graph-Based Method for Active Outlier Detection With Limited Expert Feedback
IEEE Access, 2019 Labeled data, particularly for the outlier class, are difficult to obtain. Thus, outlier detection is typically regarded as an unsupervised learning problem. However, it still has an opportunity to obtain few labeled data.
Yongmou Li +4 more
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RODA: A Fast Outlier Detection Algorithm Supporting Multi-Queries
IEEE Access, 2021 Outlier detection is an important task in the field of big data analysis. The technology has been extensively used in network security, sensor data analysis, public health and so on.
Xite Wang, Jiafan Li, Mei Bai, Qian Ma
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A Probabilistic Transformation of Distance-Based Outliers
Machine Learning and Knowledge Extraction, 2023 The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context.
David Muhr +2 more
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A new outlier detection algorithm based on observation-point mechanism
Shenzhen Daxue xuebao. Ligong ban, 2022 Outlier detection is an important branch of data mining research, and has wide applications in the fields of finance, telecommunications, and biology. The traditional nearest neighbor-based outlier detection (NNOD) and local outlier factor-based outlier ...
YU Wanguo, HE Yulin, QIN Huilin
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An integrated approach for identifying wrongly labelled samples when performing classification in microarray data. [PDF]
PLoS ONE, 2012 Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently.
Yuk Yee Leung +2 more
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