Ensemble stacking classification methods use a meta-learner to combine the results of several classifiers in order to enhance robustness and overall performance. This paper proposes a game theoretic approach to combining the results by improving the marginal contribution of each classifier to the accuracy of the meta-model. The equilibrium of the game is approximated by using a differential evolution algorithm. Numerical experiments illustrate the behavior of the approach on a set of benchmark classification problems.