Converting SVDD Scores into Probability Estimates

M El Azami (1) , Carole Lartizien (1) , Stéphane Canu (2)

1 Images et Modèles
2 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

To enable post-processing, the output of a support vector data description (SVDD) should be a calibrated probability as done for SVM. Standard SVDD does not provide such probabilities. To create probabilities , we first generalize the SVDD model and propose two calibration functions. The first one uses a sigmoid model and the other one is based on a generalized extreme distribution model. To estimate calibration parameters , we use the consistency property of the estimator associated with a single SVDD model. A synthetic dataset and datasets from the UCI repository are used to compare the performance against a robust kernel density estimator.

Domaines: Traitement du signal et de l'image [eess.SP]; Machine Learning [stat.ML]

M El Azami, Carole Lartizien, Stéphane Canu. Converting SVDD Scores into Probability Estimates. 24th European Symposium on Articial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2016, Bruges, Belgium. pp.483-488. ⟨hal-01438026⟩