3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in outlier-ensembles-20
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PyOD provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers.
The empirical results show that this approach outperforms state-of-the-art selective outlier ensemble techniques on the problem of selecting ensemble components onto a mining problem in a graph.
An unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most competent base detectors, with an emphasis on data locality.
A framework, called Locally Selective Combination in Parallel Outlier Ensembles (LSCP), is proposed which addresses the issue of reliable base detectors during model combination by defining a local region around a test instance using the consensus of its nearest neighbors in randomly selected feature subspaces.
The proposed modular acceleration system, called SUOD, focuses on three complementary acceleration aspects ( data reduction for high-dimensional data, approximation for costly models, and taskload imbalance optimization for distributed environment), while maintaining performance accuracy.
Adding a benchmark result helps the community track progress.