One-Class SVM

One-Class SVM detects anomalies by identifying cases that deviate from normal data patterns.

Oracle Machine Learning uses SVM as the one-class classifier for anomaly detection. When SVM is used for anomaly detection, it has the classification machine learning technique but no target.

One-class SVM models, when applied, produce a prediction and a probability for each case in the scoring data. If the prediction is 1, the case is considered typical. If the prediction is 0, the case is considered anomalous. This behavior reflects the fact that the model is trained with normal data.

You can specify the percentage of the data that you expect to be anomalous with the SVMS_OUTLIER_RATE build setting. If you have some knowledge that the number of "suspicious" cases is a certain percentage of your population, then you can set the outlier rate to that percentage. The model approximately identifies that many "rare" cases when applied to the general population.