One-Class SVM node

The One-Class SVM© node uses an unsupervised learning algorithm. The node can be used for novelty detection. It will detect the soft boundary of a given set of samples, to then classify new points as belonging to that set or not. This One-Class SVM modeling node is implemented in Python and requires the scikit-learn© Python library.

For details about the scikit-learn library, see http://contrib.scikit-learn.org/imbalanced-learn/about.html1.

The Modeling tab on the palette contains the One-Class SVM node and other Python nodes.

Note: One-Class SVM is used for usupervised outlier and novelty detection. In most cases, we recommend using a known, "normal" dataset to build the model so the algorithm can set a correct boundary for the given samples. Parameters for the model – such as nu, gamma, and kernel – impact the result significantly. So you may need to experiment with these options until you find the optimal settings for your situation.

1Smola, Schölkopf. "A Tutorial on Support Vector Regression." Statistics and Computing Archive, vol. 14, no. 3, August 2004, pp. 199-222. (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.4288)