Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers

So far few theoretical works investigated the conditions under which specific fusion rules can work well, and a unifying framework for comparing rules of different complexity is clearly beyond the state of the art. A clear theoretical comparison is lacking even if one focuses on specific classes of combiners (e.g., linear combiners). In this paper, we theoretically compare simple and weighted averaging rules for fusion of imbalanced classifiers. Continuing the work reported in [10], we get a deeper knowledge of classifiers' imbalance effects in linear combiners. In addition, we experimentally compare the performance of linear and order statistics combiners for ensembles with different degrees of classifiers imbalance.