Outlier Detection

11 benchmarks703 papers

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.

<span class="description-source">Source: Coverage-based Outlier Explanation </span>

Benchmarks

Outlier Detection on ECG5000

Accuracy

Outlier Detection on Balance scale_class 1

Average Accuracy

Outlier Detection on Breast cancer Wisconsin_class 2

Average Accuracy

Outlier Detection on Breast cancer Wisconsin_class 4

Average Accuracy

Outlier Detection on Fashion-MNIST

AUROC

Outlier Detection on Glass identification

Average Accuracy

Outlier Detection on Heart-C

AUC

Outlier Detection on Hepatitis

AUC-ROC

Outlier Detection on Internet Ad

AUC-ROC

Outlier Detection on Ionosphere_class b

Average Accuracy

Outlier Detection on SKAB

Average F1