methodology-6

outlier ensembles

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

PyOD: A Python Toolbox for Scalable Outlier Detection

Yue Zhao, Zain Nasrullah, Zheng Li•Sat Jan 05 2019

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.

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Graph-based selective outlier ensembles

Hamed Sarvari, C. Domeniconi, G. Stilo•Mon Apr 16 2018

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.

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LSCP: Locally Selective Combination in Parallel Outlier Ensembles

Yue Zhao, Zain Nasrullah, Zheng Li, M. K. Hryniewicki•Mon Dec 03 2018

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.

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DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

Yue Zhao•Fri Nov 22 2019

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.

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SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

Yue Zhao, Cao Xiao, Jimeng Sun, Xiyang Hu, Jianing Yang, Zheng Li, Cheng Cheng, Cong Wang, Changling wan, Wen Wang, Haoping Bai, Yunlong Wang, Zhi Qiao, L. Akoglu•Thu Dec 31 2020

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.

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