Papers outlier ensembles
“outlier ensembles” 태그가 달린 논문 7편 · 필터 해제
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of …
Decision MakingICU AdmissionKnowledge DistillationManagement+6SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection. Due to the lack of…
Dimensionality ReductionFraud DetectionIntrusion DetectionOutlier Detection+1DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles
Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth. In this paper, an unsupervised outlier detector combination fr…
outlier ensemblesPyOD: A Python Toolbox for Scalable Outlier Detection
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles …
Anomaly DetectionOutlier Detectionoutlier ensemblesLSCP: Locally Selective Combination in Parallel Outlier Ensembles
In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting compet…
Anomaly DetectionOutlier Detectionoutlier ensemblesGraph-based Selective Outlier Ensembles
An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in t…
outlier ensemblesSequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
Ensemble methods for classification and clustering have been effectively used for decades, while ensemble learning for outlier detection has only been studied recently. In this work, we design a new ensemble approach for…
Binary ClassificationClusteringEnsemble LearningGeneral Classification+2