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Article Dans Une Revue Neurocomputing Année : 2017

Converting SVDD scores into probability estimates: Application to outlier detection

M El Azami
  • Fonction : Auteur
Carole Lartizien
S. Canu

Résumé

To enable post-processing, the output of a support vector data description (SVDD) should be transformed into a calibrated probability, as it can be done for SVM. But standard SVDD only estimate a single level set and do not provide such probabilities. We present a method for estimating these probabilities from SVDD scores. The first step of our approach uses a generalization of the SVDD model that estimate simultaneously various coherent level sets. Then we introduce two calibration mechanisms for converting these level sets into probabilities. A synthetic dataset and datasets from the UCI repository are used to compare the performance of our method against a robust kernel density estimator in an outlier detection task, illustrating the interest of our approach.
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Dates et versions

hal-01592016 , version 1 (22-09-2017)

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M El Azami, Carole Lartizien, S. Canu. Converting SVDD scores into probability estimates: Application to outlier detection. Neurocomputing, 2017, 268, pp.64 - 75. ⟨10.1016/j.neucom.2017.01.103⟩. ⟨hal-01592016⟩
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