- Type Parameters:
V- Object type
@Reference(authors="B. Sch\u00f6lkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson", title="Estimating the support of a high-dimensional distribution", booktitle="Neural computation 13.7", url="https://doi.org/10.1162/089976601750264965", bibkey="DBLP:journals/neco/ScholkopfPSSW01") public class OCSVM<V> extends java.lang.Object implements OutlierAlgorithmOutlier-detection using one-class support vector machines.
Important note: from literature, the one-class SVM is trained as if 0 was the only counterexample. Outliers will only be detected when they are close to the origin in kernel space! In our experience, results from this method are rather mixed, in particular as you would likely need to tune hyperparameters. Results may be better if you have a training data set with positive examples only, then apply it only to new data (which is currently not supported in this implementation, it assumes a single-dataset scenario).
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson
Estimating the support of a high-dimensional distribution
Neural computation 13.7
- Erich Schubert
All Methods Instance Methods Concrete Methods Modifier and Type Method Description
getInputTypeRestriction()Get the input type restriction used for negotiating the data query.
run(Relation<V> relation)Run one-class SVM.
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public TypeInformation getInputTypeRestriction()Description copied from interface:
AlgorithmGet the input type restriction used for negotiating the data query.