Table 2 Test results of sl-Pauta, iForest, OCSVM, and KNN on the raw synthetic dataset.

From: Machine learning-based anomaly detection of groundwater microdynamics: case study of Chengdu, China

 

TP

FP

FN

TN

sl-Pauta

27

6

8

3609

iForest

31

7

4

3608

OCSVM

32

4

3

3611

KNN

29

7

6

3608

  1. TP number of accurately detected abnormal values, FP number of normal values defined as abnormal, FN number of abnormal values defined as normal, TN number of normal values defined as normal.
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