Results 41 to 50 of about 4,573,641 (310)
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
doaj +1 more source
Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow [PDF]
, 2019 Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection. This paper explores advances in the outlier detection area
Belhadi, Asma +4 more
core +3 more sources
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
doaj +1 more source
A Review on Outlier/Anomaly Detection in Time Series Data [PDF]
ACM Computing Surveys, 2020 Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series.
Ane Blázquez-García +3 more
semanticscholar +1 more source
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
doaj +1 more source
Outlier Mining Methods Based on Graph Structure Analysis [PDF]
, 2019 Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms.
Almeira, Nahuel +2 more
core +3 more sources
Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps
Big Data and Cognitive Computing, 2020 Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection.
Menglu Li, Rasha Kashef, Ahmed Ibrahim
doaj +1 more source
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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Detecting outlier samples in microarray data [PDF]
, 2009 In this paper, we address the problem of detecting outlier samples with highly different expression patterns in microarray data. Although outliers are not common, they appear even in widely used benchmark data sets and can negatively affect microarray ...
Hung, YS, Shieh, AD
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A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method
Information, 2019 Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information.
Liying Wang +5 more
doaj +1 more source