  Classifier ensembles are pattern recognition structures composed of a set of
classification algorithms (members), organized in a parallel way, and a
combination method with the aim of increasing the classification accuracy of a
classification system. In this study, we investigate the application of a
generalized mixture (GM) functions as a new approach for providing an efficient
combination procedure for these systems through the use of dynamic weights in
the combination process. Therefore, we present three GM functions to be applied
as a combination method. The main advantage of these functions is that they can
define dynamic weights at the member outputs, making the combination process
more efficient. In order to evaluate the feasibility of the proposed approach,
an empirical analysis is conducted, applying classifier ensembles to 25
different classification data sets. In this analysis, we compare the use of the
proposed approaches to ensembles using traditional combination methods as well
as the state-of-the-art ensemble methods. Our findings indicated gains in terms
of performance when comparing the proposed approaches to the traditional ones
as well as comparable results with the state-of-the-art methods.
