Results 31 to 40 of about 13,715 (249)
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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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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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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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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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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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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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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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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