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Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective
In this work, we design a new ensemble approach for outlier detection in multi-dimensional point data, which provides improved accuracy by reducing error through both bias and variance.
Less is More: Building Selective Anomaly Ensembles
In this work, we tap into this gap and propose a new ensemble approach for anomaly mining, with application to event detection in temporal graphs.
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