Ensemble Pruning via Constrained Eigen-Optimization

An ensemble is composed of a set of base learners that make predictions jointly. The generalization performance of an ensemble has been justified both theoretically and in practice. However, existing ensemble learning methods sometimes produce unnecessarily large ensembles, with an expense of extra computational costs and memory consumption. The purpose of ensemble pruning is to select a subset of base learners with comparable or better prediction performance. In this paper, we formulate the ensemble pruning problem into a combinatorial optimization problem with the goal to maximize the accuracy and diversity at the same time. Solving this problem exactly is computationally hard. Fortunately, we can relax and reformulate it as a constrained eigenvector problem, which can be solved with an efficient algorithm that is guaranteed to converge globally. Convincing experimental results demonstrate that this optimization based ensemble pruning algorithm outperforms the state-of-the-art heuristics in the literature.