Results 21 to 30 of about 84,843 (251)
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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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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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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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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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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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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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
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Clustering-Based Outlier Detection Technique Using PSO-KNN
Journal of Applied Science and Engineering, 2023 In this work, we present an unsupervised machine learning algorithm for outlier detection by integrating Particle Swarm Optimization (PSO) and the K-nearest neighbor (KNN) technique.
Sushilata D. Mayanglambam +2 more
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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
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