Ensemble Learning with Highly Variable Class-Based Performance
This paper proposes a novel model-agnostic method for weighting the outputs of base classifiers in machine learning (ML) ensembles.Our approach uses class-based weight coefficients assigned to every output class in each learner in the ensemble.This is particularly useful when the base classifiers have highly Bruschetta variable performance across c