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Showing 1 to 9 of 9 for “"Local outlier factor"”.
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On pruning and feature engineering in Random Forests.
… RF. These techniques are clustering, the local outlier factor, diversified weighted subspaces, and replicator dynamics. Applying these techniques on RF produced four extensions which we have termed CLUB-DRF, LOFB-DRF, DSB-RF, and RDB-DR respectively. Experimental studies on 15 real …
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Anomaly detection methods for unmanned underwater vehicle performance data
… Estimation (KDE) Anomaly Detection and (2) Local Outlier Factor. Results are presented for selected UUV systems and data features, and initial findings provide insight into the effectiveness of these algorithms. Lastly, we explore ways to extend our KDE anomaly detection algorithm for …
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An Axiomatic Perspective on Anomaly Detection
… demonstrate that another common method, Local Outlier Factor, is vulnerable to adversarial data poisoning. To conduct these experimental evaluations, a tool for dataset generation, experimentation and visualization was built, which is an additional contribution of this work.
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Graph neural network approaches and real-time unsupervised learning for anomaly detection in vehicular networks
… K-Means clustering algorithm integrated with outlier detection methods (One-Class SVM, Local Outlier Factor, Isolation Forest, and Elliptic Envelope). The proposed approach incorporates centroid repulsion, dynamic buffer normalization, and outlier-score-based thresholding, enabling adaptive …
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CARAD: Computer-aided Analysis of Radio Astronomy Data
… integrating COSFIRE descriptors with the Local Outlier Factor algorithm successfully identifies unusual radio galaxy morphologies, achieving a geometric mean of 79%, surpassing the performance of computationally intensive deep learning autoencoders. The COSFIRE methodology’s intrinsic …
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A comparative evaluation of unsupervised anomaly detection techniques in smart water metering networks
… are k-Nearest Neighbor (kNN), cluster-based local outlier factor (CBLOF), and the histogram-based outlier score (HBOS). The comparative study aims at providing a better unsupervised anomaly detection technique that can be adopted in SWMNs. This work aimed to find a better anomaly detection …
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Mining intrusion detection alert logs to minimise false positives & gain attack insight
… novel metrics, Outmet is based on the well known Local Outlier Factor algorithm. Our findings showed that with a slight trade-off of sensitivity (i.e. true positives performance), outmet reduces false positives significantly. In comparison to prior state-of-the-art, our findings show that it …
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Signal Processing Methods for Reliable Extraction of Neural Responses in Developmental EEG
… processing methods applied to newborn EEG: 1) Local Outlier Factor (LOF) for detecting and removing bad/noisy channels; 2) Artifacts Subspace Reconstruction (ASR) for detecting and removing or correcting bad/noisy segments. Then, based on these algorithms and other preprocessing …
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Optimization in Deep Learning: Structured, Realistic and Interpretable Learning for Decision-Making
… alignment by reformulating the highly nonlinear Local Outlier Factor (LOF) metric as a set of mixed-integer constraints. To address the computational challenge, we leverage the geometry of the network and propose an efficient decomposition scheme that reduces the initial hard-to-solve problem …