A Bagging algorithm for imprecise classification in cost-sensitive scenarios

Serafín Moral-García, Andrés R. Masegosa, Joaquín Abellán

Abstract: Highlights•Ensembles of classifiers tend to outperform individual learners.•The first ensemble for cost sensitive Imprecise Classification is presented.•A fusion technique that considers information and risk is proposed.•Experiments reveal that our proposal achieves satisfactory performance.