Results 31 to 40 of about 84,843 (251)
Journal of Data Science, Statistics, and Visualisationnew anomaly detection method called kernel outlier detection (KOD) is proposed.It is designed to address challenges of outlier detection in high-dimensionalsettings. The aim is to overcome limitations of existing methods, such as dependenceon distributional assumptions or on hyperparameters that are hard to tune.KOD starts with a kernel transformation,
Can Hakan Dagidir +2 more
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International Journal of Distributed Sensor Networks, 2015 Since the nonstationary distribution of the detected objects is general in the real world, the accurate and efficient outlier detection for data analysis within wireless sensor network (WSN) is a challenge.
Haiqing Yao, Heng Cao, Jin Li
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Advancements of Outlier Detection: A Survey
EAI Endorsed Transactions on Scalable Information Systems, 2013 Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large datasets. In this paper, we present a survey of outlier detection techniques to reflect the recent advancements
Ji Zhang
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BMC Medical Research Methodology, 2023 Background Growth studies rely on longitudinal measurements, typically represented as trajectories. However, anthropometry is prone to errors that can generate outliers.
Paraskevi Massara +9 more
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Speedup Two-Class Supervised Outlier Detection
IEEE Access, 2018 Outlier detection is an important topic in the community of data mining and machine learning. In two-class supervised outlier detection, it needs to solve a large quadratic programming whose size is twice the number of samples in the training set.
Yugen Yi +3 more
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An Efficient Density-Based Local Outlier Detection Approach for Scattered Data
IEEE Access, 2019 After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore
Shubin Su +6 more
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Entropy, 2023 Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data ...
Zihao Li, Liumei Zhang
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Conserved binding mode but diverse interfaces of MreC‐PBP2 interactions
FEBS Letters, EarlyView.The crystal structure of abMreC reveals a conserved two β‐barrel architecture and provides structural insights into its role within the bacterial elongasome. The abMreC–abPBP2 complex model identifies the molecular basis of MreC‐mediated PBP2 recognition, contributing to the regulation of peptidoglycan synthesis.
Hyunseok Jang +4 more
wiley +1 more source
A Survey on Mixed-Attribute Outlier Detection Methods
CommIT Journal, 2019 In the data era, outlier detection methods play an important role. The existence of outliers can provide clues to the discovery of new things, irregularities in a system, or illegal intruders.
Nur Rokhman
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Molecular Oncology, EarlyView.We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric +10 more
wiley +1 more source