Multi-class probabilistic classification using inductive and cross Venn–Abers predictors

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Valery Manokhin ;
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:228-240, 2017.

Abstract

Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems. We present a new approach to multi-class probability estimation by turning IVAPs and CVAPs into multi-class probabilistic predictors. The proposed multi-class predictors are experimentally more accurate than both uncalibrated predictors and existing calibration methods.

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