This is an official implementation of the following paper:

Han Bao and Masashi Sugiyama. Fenchel-Young Losses with Skewed Entropies for Class-posterior Probability Estimation. In AISTATS, 2021. [link]

The paper provides a convex loss for CPE (class-posterior probability estimation) under class-imbalance, based on Fenchel-Young losses.

pip install -r requirements.txt

python main.py loss.name=gev_fenchel_young dataset=test

python f_measure.py loss.name=gev_fenchel_young dataset=test

The following methods can be tested (specified for loss.name):

• gev_fenchel_young: GEV-Fenchel-Young loss
• gev_canonical: GEV-canonical loss
• gev_log: GEV-log loss
• logistic: logistic regression
• hinge: hinge loss with Platt's scaling
• isotonic: probability calibration with isotonic regression
• weight: balanced logistic regression
• bagging: undersampling with bagging

Please refer to the supplementary material of the paper to see details.

The following datasets are available (specified for dataset): car, ecoli, glass, haberman, nursery, and yeast.

More options are available at config.yaml.