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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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Context-aware Adaptive Outlier Detection in Trajectory Data
, 2020 With the advent of data mining and business processes automation, outlier detection has evolved into a major problem attracting significant research in relation to several application domains.
Wenbin Zhang +9 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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Fluctuation-based outlier detection
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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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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Effective and Robust Boundary-Based Outlier Detection Using Generative Adversarial Networks
, 2022 Outlier detection aims to identify samples that do not match the expected patterns or major distribution of the dataset. It has played an important role in many domains such as credit card fraud identification, network intrusion detection, medical image ...
Liang Chang +11 more
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Outlier detection by logic programming [PDF]
ACM Transactions on Computational Logic, 2007 The development of effective knowledge discovery techniques has become a very active research area in recent years due to the important impact it has had in several relevant application domains. One interesting task therein is that of singling out anomalous individuals from a given population, for example, to detect rare events in time-series analysis ...
ANGIULLI, Fabrizio +2 more
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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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Geodesy and cartography, 2012 Up-to-date digital photogrammetry involves operations on huge data sets, and with classical image processing procedures it might be time consuming to find out the best solution. One of the key tasks is to detect outliers in given data, eg for curve fitting or image matching. The problem is hard as the number of outliers is usually large, possibly
Ruzgienė, Birutė, Förstner, Wolfgang
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An Innovative Outlier Detection Method Using Localized Thresholds [PDF]
, 2012 In this paper, we investigate the research problem of specifying the thresholds to effectively detect outliers and propose an innovative technique that leverages multiple localized thresholds for detecting outliers.
Cao, Jie +5 more
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