Results 11 to 20 of about 4,573,641 (310)
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
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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Progress in Outlier Detection Techniques: A Survey
IEEE Access, 2019 Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and
Hongzhi Wang +2 more
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ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2022 Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability ...
Zheng Li +5 more
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PyGOD: A Python Library for Graph Outlier Detection [PDF]
Journal of machine learning research, 2022 PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based methods for outlier detection under an easy-to-use, well-documented API ...
Kay Liu +12 more
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Out-of-distribution Detection with Implicit Outlier Transformation [PDF]
International Conference on Learning Representations, 2023 Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typically deviate from test OOD data. Thus, the performance of OE, when facing
Qizhou Wang +7 more
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Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
Applied Sciences, 2021 Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns.
Michael Heigl +3 more
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Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection [PDF]
IEEE International Conference on Data Engineering, 2022 Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable ...
Tung Kieu +6 more
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Outlier Detection in High Dimensional Data
Regular Issue, 2021 Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception.
C. Aggarwal, Philip S. Yu
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LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2021 Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN.
Adam Goodge +3 more
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