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ACM Computing Surveys, 2020
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors.
Azzedine Boukerche +2 more
openaire +2 more sources
Over the past decade, we have witnessed an enormous amount of research effort dedicated to the design of efficient outlier detection techniques while taking into consideration efficiency, accuracy, high-dimensional data, and distributed environments, among other factors.
Azzedine Boukerche +2 more
openaire +2 more sources
Encyclopedia of Social Network Analysis and Mining, 2014
Yufeng Kou, Chang-Tien Lu
semanticscholar +3 more sources
Yufeng Kou, Chang-Tien Lu
semanticscholar +3 more sources
Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang +2 more
+4 more sources
Outlier detection is a fundamental issue in data mining and machine learning. Most methods calculate outlier score for each object and then threshold the scores to detect outliers. Most widely used thresholding techniques are based on statistics like standard deviation around mean, median absolute deviation and interquartile range. Unfortunately, these
Jiawei Yang +2 more
+4 more sources
WIREs Data Mining and Knowledge Discovery, 2011
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih‐Ling Tsai
openaire +2 more sources
AbstractOutlier detection is an area of research with a long history which has applications in many fields. This article provides a nontechnical and concise overview of the commonly used approaches for detecting outliers, including classical methods, new challenges posed by real‐world massive data, and some of the key advances made in recent years ...
Xiaogang Su, Chih‐Ling Tsai
openaire +2 more sources
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
Neural Information Processing Systems, 2023Out-of-distribution (OOD) detection is important for deploying reliable machine learning models on real-world applications. Recent advances in outlier exposure have shown promising results on OOD detection via fine-tuning model with informatively sampled
Jianing Zhu +6 more
semanticscholar +1 more source
Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods
Sensors, 2020 Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction
Muhammad Fazal Ijaz +2 more
exaly +2 more sources
Multigranulation Relative Entropy-Based Mixed Attribute Outlier Detection in Neighborhood Systems
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022Outlier detection is widely used in many fields, such as intrusion detection, credit card fraud detection, medical diagnosis, and so on. Existing outlier detection algorithms are mostly designed for dealing with numeric or categorical attributes. However,
Zhong Yuan +4 more
semanticscholar +1 more source