一种基于边界的贪心组合剪枝方法

理论及实验表明，在训练集上具有较大边界分布的组合分类器泛化能力较强。文中将边界概念引入到组合剪枝中，并用它指导组合剪枝方法的设计。基于此，构造一个度量标准(MBM)用于评估基分类器相对于组合分类器的重要性，进而提出一种贪心组合选择方法(MBMEP)以降低组合分类器规模并提高它的分类准确率。在随机选择的30个UCI数据集上的实验表明，与其它一些高级的贪心组合选择算法相比，MBMEP选择出的子组合分类器具有更好的泛化能力。

A Margin-Based Greedy Ensemble Pruning Method

Theoretical and experimental results indicate that for the ensemble classifiers with the same training error the one with higher margin distribution on training examples has better generalization performance. Therefore,the concept of margins of examples is introduced to ensemble pruning and it is employed to supervise the design of ensemble pruning methods. Based on the margins,a new metric called margin based metric (MBM) is designed to evaluate the importance of a classifier to an ensemble and an example set,and then a greedy ensemble pruning method called MBM based ensemble selection is proposed to reduce the ensemble size and improve its accuracy. The experimental results on 30 UCI datasets show that compared with other state of the art greedy ensemble pruning methods,the ensembles selected by the proposed method have better performance.