Boosting fuzzy rules with low quality data in multi-class problems: Open problems and challenges

Abstract

Existing extensions of AdaBoost-based fuzzy rule learning to low quality databases yield suboptimal results in multi-class problems. A new procedure is proposed where the original multi-class database is transformed into several multi-label problems that can be tackled with binary AdaBoost. The performance of this proposal is assessed in comparison with other classification schemes for imprecise data. A novel experimental design for imprecise databases is introduced for this last purpose. The new algorithm is applied to a set of real-world and synthetic low quality datasets.