2 research outputs found
Consistent Classification with Generalized Metrics
We propose a framework for constructing and analyzing multiclass and
multioutput classification metrics, i.e., involving multiple, possibly
correlated multiclass labels. Our analysis reveals novel insights on the
geometry of feasible confusion tensors -- including necessary and sufficient
conditions for the equivalence between optimizing an arbitrary non-decomposable
metric and learning a weighted classifier. Further, we analyze averaging
methodologies commonly used to compute multioutput metrics and characterize the
corresponding Bayes optimal classifiers. We show that the plug-in estimator
based on this characterization is consistent and is easily implemented as a
post-processing rule. Empirical results on synthetic and benchmark datasets
support the theoretical findings
Fairness with Overlapping Groups
In algorithmically fair prediction problems, a standard goal is to ensure the
equality of fairness metrics across multiple overlapping groups simultaneously.
We reconsider this standard fair classification problem using a probabilistic
population analysis, which, in turn, reveals the Bayes-optimal classifier. Our
approach unifies a variety of existing group-fair classification methods and
enables extensions to a wide range of non-decomposable multiclass performance
metrics and fairness measures. The Bayes-optimal classifier further inspires
consistent procedures for algorithmically fair classification with overlapping
groups. On a variety of real datasets, the proposed approach outperforms
baselines in terms of its fairness-performance tradeoff