Majority vote ensembles of conformal predictors
Giovanni Cherubin · Machine Learning · 2018
We study majority vote ensembles of $$\varepsilon $$ -valid conformal predictors (CP). We show that the prediction set $$\varGamma ^\eta $$ produced as the majority vote among the prediction sets $$\varGamma ^\varepsilon _i$$ of k independent $$\varepsilon $$ -valid CPs is also valid, for some significance level $$\eta $$ ; we provide a method to compute $$\varepsilon $$ to achieve a desired $$\eta $$ . We further indicate an error upper bound for an ensemble of correlated CPs, and derive a value $$\varepsilon $$ for which such an ensemble guarantees $$\eta $$ conservative validity. We evaluate empirically our findings, and compare them with alternative strategies for combining CPs’ predictions.