Maximizing diversity by transformed ensemble learning

The diversity and the individual accuracies in an ensemble system are usually two opposite objects, which is ignored in most preliminary ensemble learning algorithms. To alleviate this issue, in this paper, a novel weighted ensemble learning is proposed by maximizing the diversity and the individual accuracy simultaneously. More specifically, in the proposed framework, the combination of multiple base learners is converted into a linear transformation of all these base learners, and the optimal weights are obtained by pursuing the optimal projective direction of the linear transformation. Then the derived objective function can be efficiently solved by the alternating directional multiplier method. Finally, the proposed method is verified on UCI datasets and face databases, and the experimental results illustrate that the proposed method effectively improves the performance compared with other ensemble methods. •A novel ensemble learning is proposed to balance diversity and individual accuracy.•The whole process can be implemented as the linear transforms of individuals.•The derived objective function can be efficiently solved by an ADMM-like algorithm.