SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

No papers found.

Benchmark Results

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1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
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1ASVDDAverage Accuracy99.03—Unverified
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1ASVDDAverage Accuracy37.62—Unverified
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1ASVDDAverage Accuracy65.6—Unverified
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1PAEAUROC1—Unverified
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1ASVDDAverage Accuracy99.05—Unverified
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1MIXAUC0.86—Unverified
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1MIXAUC-ROC0.85—Unverified
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1MIXAUC-ROC0.93—Unverified
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1ASVDDAverage Accuracy86.33—Unverified
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1LSTMCapsAverage F10.74—Unverified