The main principle of stacked generalization (or Stacking) is using a
second-level generalizer to combine the outputs of base classifiers in an
ensemble. In this paper, we investigate different combination types under the
stacking framework; namely weighted sum (WS), class-dependent weighted sum
(CWS) and linear stacked generalization (LSG). For learning the weights, we
propose using regularized empirical risk minimization with the hinge loss. In
addition, we propose using group sparsity for regularization to facilitate
classifier selection.