Optimal Rates for Nonparametric F-Score Binary Classification via Post-Processing

Evgenii Chzhen (1, 2)

1 CELESTE - Statistique mathématique et apprentissage
2 LMO - Laboratoire de Mathématiques d'Orsay

This work studies the problem of binary classification with the F-score as the performance measure. We propose a post-processing algorithm for this problem which fits a threshold for any score base classifier to yield high F-score. The post-processing step involves only unlabeled data and can be performed in logarithmic time. We derive a general finite sample post-processing bound for the proposed procedure and show that the procedure is minimax rate optimal, when the underlying distribution satisfies classical nonparametric assumptions. This result improves upon previously known rates for the F-score classification and bridges the gap between standard classification risk and the F-score. Finally, we discuss the generalization of this approach to the set-valued classification.

Domaines: Théorie [stat.TH]

Evgenii Chzhen. Optimal Rates for Nonparametric F-Score Binary Classification via Post-Processing. Mathematical Methods of Statistics, 2021, ⟨10.3103/S1066530720020027⟩. ⟨hal-02123314⟩